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Policy AnalysisMarch 9, 2023

Closing the Gap between Carbon Neutrality Targets and Action: Technology Solutions for China’s Key Energy-Intensive Sectors
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Jinchi Dong
Jinchi Dong
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China
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https://orcid.org/0000-0001-8869-8818
Bofeng Cai
Bofeng Cai
Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
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Shaohui Zhang*
Shaohui Zhang
School of Economics and Management, Beihang University, Beijing 100191, China
International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
*Shaohui Zhang Email: [email protected]. Corresponding author address: School of Economics and Management, Beihang University, Beijing 100191, China.
More by Shaohui Zhang
https://orcid.org/0000-0003-2487-8574
Jinnan Wang*
Jinnan Wang
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China
Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
*Jinnan Wang Email: [email protected]. Tel.: +86 10 84910681. Fax: +86 10 84918581. Corresponding author address: Chinese Academy of Environmental Planning, Beijing 100012, China; School of the Environment, Nanjing University, Nanjing, Jiangsu, 210023, China.
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Hui Yue
Hui Yue
Center for Energy, Environment & Economy Research, School of Management, Zhengzhou University, Zhengzhou 450001, China
Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
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Can Wang
Can Wang
State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China
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https://orcid.org/0000-0002-1136-792X
Xianqiang Mao
Xianqiang Mao
School of Environment, Beijing Normal University, Beijing 100875, China
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https://orcid.org/0000-0001-5924-8180
Jianhui Cong
Jianhui Cong
School of Economics and Management, Shanxi University, Taiyuan 030000, China
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Fei Guo
Fei Guo
International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2023, 57, 11, 4396–4405

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https://doi.org/10.1021/acs.est.2c08171

Published March 9, 2023

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Abstract

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Facing significant carbon emissions annually, China requires a clear decarbonization strategy to meet its climate targets. This study presents a MESSAGEix-CAEP model to explore Chinese decarbonization pathways and their cost-benefit under two mitigation scenarios by establishing connections between five energy-intensive sectors based on energy and material flows. The results indicated the following: 1) Interaction and feedback between sectors should not be disregarded. The electrification process of the other four sectors was projected to increase electricity production by 206%, resulting in a higher power demand than current forecasts. 2) The marginal abatement cost to achieve carbon neutrality across all five sectors was 2189 CNY/tCO₂, notably higher than current Chinese carbon emission trading prices. 3) The cost-benefit analysis indicates that a more ambitious abatement strategy would decrease the marginal abatement cost and result in a higher net carbon abatement benefit. The cumulative net benefit of carbon reduction was 7.8 trillion CNY under ambitious mitigation scenario, 1.3 trillion CNY higher than that under current Chinese mitigation scenario. These findings suggest that policy-makers should focus on the interaction effects of decarbonization pathways between sectors and strengthen their decarbonization efforts to motivate early carbon reduction.

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Synopsis

Pathways to achieve Chinese carbon reduction targets remain ambiguous. This study reveals clear decarbonization strategies for industries and their cost-benefits based on the MESSAGEix-CAEP model.

1. Introduction

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Anthropogenic climate change resulting from carbon emissions has been the main driver of weather and climate extremes. (1) According to the Intergovernmental Panel on Climate Change (IPCC), the frequency and intensity of these extremes have strengthened since the 1950s and affected every inhabited region across the globe. (1) Without additional measures to reduce carbon emissions, the global mean surface temperature is expected to increase by 3.3 to 5.7 °C by the end of the century, leading to more severe climate changes and socioeconomic losses. (1) To prevent dangerous human interference with the climate system, the international community adopted the United Nations Framework Convention on Climate Change (UNFCCC) in 1992, serving as the principal forum for cooperation among nations on greenhouse gas (GHG) induced climate change. Building on the Convention, the 196 parties adopted the Paris Agreement at the 2015 UN Climate Change Conference and, for the first time, undertook to combat climate change by limiting the global temperature rise to well below 2 °C and striving for 1.5 °C.

As a major contributor to global CO₂ emissions, accounting for approximately 30% of global emissions in 2020, China’s actions are expected to play a vital role in achieving global temperature control targets. (2) To reduce China’s carbon emissions, the Chinese government has committed to reaching peak CO₂ emissions before 2030, which would result in a 60–65% reduction in emission intensity compared with its 2005 level, and to achieving carbon neutrality by 2060. These are grand targets that China has just 30 years from carbon peaking to achieve carbon neutrality, a shorter time frame compared with developed countries such as the United States (43 years), the United Kingdom (59 years), and Germany (60 years).

A rapidly growing body of research has explored various decarbonization pathways for China to achieve its carbon peaking and neutrality targets. Some researchers have used socioeconomic models, such as LMDI, IPAT, and LEAP, to analyze the drivers that affect regional carbon peaking and carbon neutrality and to predict the pathways by combining them with the Kuznets curve or following the trends underlying the associated drivers. (3−8) Although these methods provide a macro forecast of time, amount of carbon peaking or carbon neutrality, they do not provide specific industries and technology development pathways to support the formulation of decarbonization policies for each industry. In addition, these driver-based models assume that industry developments follow a predefined and independent pathway and fail to establish connections between industries when analyzing multiple industries’ decarbonization pathways. (9) Given that industries are highly interconnected, this lack of interindustry feedback and transparent technology development pathways in these studies has produced widely divergent and puzzling results. (10)

Other researchers have used various integrated assessment models (IAMs), such as the GCAM, (11) IMAGE, (12) and TIMS models, (9) to explore Chinese decarbonization pathways. These studies provide valuable foundational research and insight into Chinese decarbonization pathways and technology development trends. Nevertheless, a clear decarbonization pathway for China remains uncertain. For example, Duan et al. (13) conducted a comprehensive analysis of Chinese decarbonization pathways using nine IAMs and discovered that negative emission technologies play an important role in achieving near-zero emissions, with captured carbon accounting on average for 20% of the total reductions in 2050. However, Zhang et al. (9) suggest early peaking pathways to prevent overreliance on carbon removal technologies and reduce the transition burden since the negative technologies have higher investment costs. van Sluisveld et al. (12) also advocate that more analysis is needed to study a broader palette of conceivable decarbonization pathways and implications to improve the optimal decarbonization policy formulation. In addition, given that the carbon trading market is regarded as a useful market method to motivate carbon reduction, determining marginal abatement costs for overall and each key energy-intensive sector is necessary to guide China’s current and future national carbon trading market. (14,15)

To bridge these research gaps, in this study, we assessed the decarbonization pathways and costs of five energy-intensive sectors in China: the power, iron and steel, cement, road traffic, and building sectors. To address the data availability difficulties regarding industry abatement technologies, we developed the China Carbon Neutrality Technology Database (CNTD), an open-access, transparent platform containing cost, operational, and abatement parameters for more than eight industries. Based on the CNTD platform, we analyzed the decarbonization pathways of the industries under two mitigation scenarios linked to SSP2 (Shared Socioeconomic Pathway 2) and the Chinese carbon peaking and carbon neutrality target. Instead of using a predetermined commodity demand as the model input, we utilized material flows to establish connections between different industries and predicted the product demand for upstream industries through the material flow. We used the Model for Energy Supply Systems And their General Environmental impact (MESSAGE) to model the material flow and analyze the optimal technology development pathways under the cost minimization objective function. Finally, we analyzed the marginal abatement cost of the industries by exploiting the expert-based marginal abatement cost and conducted a cost-benefit analysis under the mitigation scenarios.

2. Methods and Data

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2.1. China Carbon Neutrality Technology Database

To achieve carbon neutrality and the 1.5 °C temperature control target, a radical transformation of human activities is imperative. (16) According to International Energy Agency, various strategies for carbon emission reduction can fall into three broad categories: nature-based solutions, measures that aim to enhance natural processes, and technology-based solutions. (17) Among these mitigation measures, technology access and adoption are regarded as the most crucial measures in achieving a decarbonized world, which is expected to reduce global greenhouse gas (GHG) emissions by 40–70% by 2050. (18) In addition, technologies that are needed to achieve deep cuts in global emissions by 2030 already exist, (19) of which costs of 100 USD/tCO₂-eq or less are sufficient to reduce global greenhouse gas emissions by at least half of the 2019 level by 2030. (18) However, there is still a lack of a database that collects carbon abatement technologies, constraining technology promotion and research on future decarbonization pathways.

To address this challenge, we developed the China Carbon Neutrality Technology Database (CNTD). It is an open-access, transparent platform (http://cntd.cityghg.com/) that aims to provide a comprehensive and transparent technology database for the improvement of “bottom-up” energy and climate integrated assessment models and serves as a platform for estimating the impact of abatement technology development on energy savings, CO₂ emissions, and costs. (The data sources for the database are detailed in the Supporting Information.)

The CNTD platform has two modules: the database and abatement cost analysis modules. The database module provides a range of carbon abatement technologies (CATs) and their parameters, such as cost, operational, and abatement parameters. The CATs are further categorized into two groups: production process technologies (PPTs) and add-on technologies (AOTs). PPTs refer to the entire production process of a product, such as the Blast Furnace-Basic Oxygen Furnace (BF-BOF) technology in the iron and steel industry, whereas AOTs refer to energy-saving technologies used in a specific part of a PPT, such as waste heat recovery technology. Based on these two types of technologies, this module allows users to analyze the macro production technology changes and the specific energy savings in each industry.

The abatement cost analysis module provides an online function that allows users to analyze the abatement cost and potential for each technology, as well as the expert-based marginal abatement cost curve and the accumulative abatement cost curve.

2.2. Modeling Framework

The Model for Energy Supply Systems And their General Environmental impact (MESSAGE) was developed by the International Institute for Applied Systems Analysis (IIASA) for over 40 years. It is a process-based integrated assessment model that provides a detailed representation of the technical engineering, socioeconomic, and biophysical processes in products, energy, and land-use systems. (20) Specifically, compared with other IAMs, such as the GCAM model, MESSAGE provides a modeling framework that combines technologies and their commodities to develop energy and material flows. As a result, the MESSAGE model is a more transparent model that can be used to represent the energy and resource footprints from extraction, beneficiation, conversion, distribution, and consumption processes, allowing for the analysis of the evolution of each technology and its energy savings and carbon reduction directly. (21) Additionally, the model optimizes energy systems with a specified demand at the lowest system cost using a cost minimization objective function. (20) This function allows us to analyze the industry decarbonization pathways with the lowest abatement costs, a basic principle adopted in this study.

MESSAGEix-CAEP was developed based on the MESSAGE modeling framework. The core feature of MESSAGEix-CAEP is the material flow, which presents a high level of detail about natural resources, energy, emissions, products, and associated technologies. Five energy-intensive industries were involved and linked by energy and material flows (Figure 1). The road traffic and building sectors are classified as terminal sectors, and the demand for their products is predicted by the equations given in Section 2.3. The power, iron and steel, and cement industries, in contrast, are classified as upstream sectors, and their product demand is predicted by the terminal sectors under the material flow. This dynamic feedback between these five sectors circumvents the need to set demand assumptions for each industry that could introduce significant uncertainties. In total, this model covers 138 technologies, including 28 PPTs and 83 AOTs for carbon abatement and 27 conventional technologies (see the Supporting Information for details).

Figure 1. MESSAGEix-CAEP model framework.

The model period for this study was from 2005 to 2060, with time intervals of one year before 2035 and five years between 2035 and 2060. We selected 2021 as the first model year to ensure that the modeling results aligned with the current technology adoption situation. We used the constraints and status (e.g., total capacity, new capacity, and total activity in the specific year) of each technology, along with their associated technical lifetimes, to avoid abrupt changes in technology application. (21)

2.3. Projection of Future “Product” Demand for the Road Traffic and Building Sector

2.3.1. Projection of Chinese Future Vehicle Ownership

Historical evidence has shown that vehicle ownership and gross domestic product (GDP) per capita present an S-shaped curve. (22−24) Vehicle ownership increases slowly at the lowest income levels and then more rapidly as income rises (income effect) and vehicle price decreases (price effect), finally slowing down as saturation is approached. (24,25) Several different functional forms are used to describe such a process, including the Gompertz function, the logistic function, and the Richards function. Among these functions, the Gompertz function has been found to be the function that better fits the historical data and is flexible in allowing different curvatures at low-income levels (such as for China). (25,26)

Therefore, we use the Gompertz function to predict Chinese future vehicle ownership. The equation is shown as follows

X_{g_{t}} = a \cdot \exp (- b \cdot \exp (- c \cdot g_{t}))

(1)

where X_{g_t} represents the quantity of vehicle ownership per 1000 people in year t; g_t is the GDP per capita in year t; and a represents the ultimate saturation level of vehicle ownership per 1000 people. b and c, determine the shape of the curve. However, as China still has not reached the inflection point and is far from saturation, the parameters of a, b, and c remain a point of contention among researchers. To address this challenge, we conduct a meta-analysis to determine the values of a, b, and c; see the Supporting Information for details.

2.3.2. Projection of Chinese Future Housing Demand

The correlation between housing area per capita and GDP per capita has been well-documented in previous studies. (27−29) To investigate this relationship, we conducted a linear regression analysis using data from 2005 to 2020, exploring the association between GDP per capita and housing area per capita. Our findings indicate that the R² value reached 0.98/0.99 between GDP per capita and rural/urban housing per capita (see the Supporting Information for details), indicating a strong relationship between these variables. Therefore, we used GDP per capita and the resulting regression coefficient to project future rural and urban housing per capita.

2.4. Scenario Design

To assess the integrated climate change mitigation pathways and costs, scenarios need to be designed considering both the socioeconomic change scenarios and mitigation effort scenarios. The mitigation effort scenarios alone generate carbon emissions or climate projections but are not interpreted as corresponding to specific societal pathways, whereas socioeconomic change scenarios are societal futures in which no climate change impacts would occur or climate policy responses are not implemented. (30) Therefore, we used the shared socioeconomic pathways (SSPs) as the societal pathways and carbon peaking in 2030 and carbon neutrality in 2060 as the mitigation effort targets in this study.

We developed three scenarios: baseline (BL), stable carbon neutrality (SCN), and high-ambition carbon neutrality (HCN). The BL scenario represented a future development in the absence of mitigation efforts, with no AOTs included, and the adoption level of PPTs remaining the same as in 2020. We chose SSP2 as the basis for the BL scenario since it is designed as the middle development road and is consistent with typical patterns of historical experience. (31) The SCN scenario follows the current Chinese mitigation efforts and assumes that carbon emissions will peak after 2025 and before 2030 and finally achieve neutrality in 2060. The HCN scenarios present a more ambitious decarbonization pathway that assumes carbon emissions will peak before 2025 and achieve neutrality in 2060. The purpose of the HCN scenario was to analyze whether a more ambitious emissions reduction in early stage would lead to achieving a higher or lower carbon abatement cost.

We used the Chinese data-based SSP2 data from Chen et al. (32) as the basic socioeconomic input for the MESSAGEix-CAEP model. Compared with the SSP2 data published by IIASA, the Chinese data-based SSP2 is relatively new and reflects the development situation in China more closely, taking into account the country’s new socioeconomic policies, such as the two-child policy released in 2016.

2.5. Marginal Abatement Cost

The marginal abatement cost is defined as the cost associated with the last unit (the marginal cost) to pay per additional unit of emissions reduction and is often depicted in the form of a marginal abatement cost curve (MACC) under a certain amount of emission abatement. (33,34) Policymakers can use the MACC to determine the carbon trading price, which is equal to the marginal abatement cost at a given abatement target. (35) In addition, the MACC can provide insight into the total abatement costs through integration of the abatement cost curve, as well as the average abatement cost by dividing the total abatement cost by the amount of abated emissions to measure the overall cost of a long-term abatement target. (34,36) Because of its simplified representation of the complex issue of cost-effective emissions, the MACC has gained significant attention as a standard policy tool for assessing the economics of climate change mitigation options. (34,37,38)

Currently, three broadly used approaches can be taken to estimate the MACC: expert-based MACC, model-derived MACC, and supply side/production-based MACC (see Du et al. (37) for details). To accurately assess the costs and potential of various abatement technologies, we utilized expert-based marginal abatement cost curves in this study to provide a detailed understanding of the abatement information on specific technologies and industries. The expert-based MACC is an engineering bottom-up approach that is used to assess the emissions reduction potential and corresponding cost of each technology based on actual production information. It, then, ranks the technology from least to most expensive to represent the costs of achieving incremental levels of emissions reductions. (37)

Specifically, we first used the following equations to calculate the total cost of each technology

{T C}_{j, t} = {A I C}_{j, t} + {F C}_{j, t} + {V C}_{j, t}

(2)

{A I C}_{j, t} = {I C}_{j, t} \times C R F

(3)

C R F = i \times {(i + 1)}^{t} / [{(i + 1)}^{t} - 1]

(4)

where TC_j,t represents the total cost of technology j at year t. FC_j,t and VC_j,t represent the fixed cost and variable cost, respectively. To calculate the annualized investment cost AIC_j,t, investment cost IC_j,t in the lifetime t of the technology is multiplied by the capital recovery factor CRF. In this study, we use the discount rate i = 0.08, and TC_j,t does not consider VC_j t for the AOTs.

Second, we calculated the carbon abatement cost for each technology by dividing the incremental cost by the emissions avoided. (39) For the AOTs, the incremental costs and emission reductions are determined by comparing the adoption of AOTs to the absence of their adoption. The parameters of the incremental costs (AOTs’ total costs, TC_j,t) and emission reductions (AOTs’ carbon abatement potential, CAP_j,t) are provided by the CNTD platform. For the PPTs, the incremental costs and emission reductions for each technology are calculated by subtracting their baseline technology’s total cost and emission factor. The equation for carbon abatement cost is as follows

{C A C}_{{P P T}_{j}, t} = \frac{Δ {T C}_{j, t}}{C {A P}_{j, t}} = \frac{{T C}_{j, t} - {T C}_{{b l}_{j}, t}}{{E F}_{{b l}_{j}, t} - {E F}_{j, t}}

(5)

{C A C}_{{A O T}_{j}, t} = \frac{{T C}_{j, t}}{C {A P}_{j, t}}

(6)

where CAC_{PPT_j, t} and CAC_{AOT_j, t} are the carbon abatement costs of PPTs and AOTs, respectively. EF_{bl_j, t} and EF_j,t are the emission factors of the baseline technology and technology j. CAP_j,t represents the carbon abatement potential of AOTs.

Finally, the expert-based marginal abatement cost curve is obtained by ranking technologies based on their carbon abatement costs. We further fit the expert-based MACC with a logarithmic function to obtain the MACC function (see the Supporting Information for details). Then, the total abatement cost is calculated by summing each technology’s abatement cost (the area of rectangles at the expert-based MACC) as an approximation of the integration of the MACC function.

3. Results

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3.1. CO₂ Emissions

Figure 2a illustrates the projected CO₂ emissions under different scenarios; we remain at 10% of 2020 carbon emissions in 2060 for carbon sequestration under the SCN and HCN scenarios. (40,41) Without carbon mitigation efforts (BL scenario), Chinese CO₂ emissions are projected to continue to grow from 2021 to 2060. By 2060, CO₂ emissions are projected to be 75.4% higher than the 2020 level. In contrast, CO₂ emissions under the current mitigation efforts (SCN scenario) are projected to peak in 2029 and achieve a cumulative carbon reduction of 54.8% from 2021 to 2060 compared with the BL scenario.

Figure 2. CO₂ emissions under the SCN scenario from 2020 to 2060. a, Proportion of each sector’s carbon reduction between the BL and SCN scenarios. b, CO₂ emissions and peak time for each sector under the HCN scenario. The carbon emissions and energy consumption in 2020 are calculated by historical data. IS: iron and steel industry; TF: road traffic sector; PW: power industry; BL: building sector; CM: cement industry.

For each sector (Figure 2b), the power industry is projected to peak its emissions by 2025, making it the first industry to achieve carbon peaking. It is followed by the cement, iron and steel, and road traffic sectors, which are projected to reach carbon peaks in 2026, 2027, and 2030, respectively. Due to the highest carbon abatement cost, the building sector is expected to be the last to implement the associated reduction measures, resulting in a projected carbon peak in 2045. Furthermore, if China imposes a more ambitious decarbonization strategy (HCN scenarios), a near-linear reduction in CO₂ emissions would be achieved, with a projected peak before 2025 and a cumulative reduction of 60.9% compared with the BL scenarios (Figure A4).

3.2. Energy Consumption

Reducing coal consumption will be a major contribution to reducing Chinese carbon emissions. Under the SCN scenarios, coal consumption is projected to decrease by 77.9% from 2020 to 2060 (Figure A6) and contribute 51.6% of the total carbon reduction by 2060 (Figure 3a). In addition, benefiting from the adoption of carbon capture and storage (CCS), technologies with coal consumption are not necessarily completely replaced. The consumption of coal is projected to remain at 13.3% of the total energy consumption in 2060 (Figure 3b). Furthermore, the consumption of gasoline and diesel is projected to peak in 2030 and 2029 and then decline smoothly (Figure A6). By 2060, gasoline and diesel consumption are projected to be close to zero, contributing to 7.2% and 20.2% of total carbon reductions, respectively.

Figure 3. Energy carbon reduction and consumption under the SCN scenario from 2020 to 2060. a, Proportion of energy carbon reduction between the BL and SCN scenarios. b, Proportion of primary energy consumption from 2020 to 2060 under the SCN scenario. The carbon emissions and energy consumption in 2020 are calculated by historical data. All energies are harmonized by converting them to standard coal (29307 kJ/kg). The consumption of renewable energies is calculated based on their power generation and converted through the theoretical power conversion coefficient (0.1229 kgce/kWh).

In contrast, under the SCN scenario, natural gas consumption is projected to exceed the consumption under the BL scenario. The share of natural gas in total energy consumption is projected to rise from 1% in 2020 to 52% in 2060, which is primarily used by the iron and steel industry for gas-based direct reduced iron (DRI) production. Under the HCN scenario, however, natural consumption is projected to decrease to zero (Figures A5, A6). This is because gas-based DRI technology has a higher carbon abatement cost, while the model prioritizes other technologies with a lower cost. The lower carbon reductions under the SCN scenario, however, would necessitate the adoption of technologies with high carbon abatement costs. Specifically, the CO₂ reduction by technologies with CCS (scrap-electric furnace steelmaking with CCS and coal-based furnace steelmaking with CCS) contributes to almost 100% of the iron and steel industry total carbon reduction under the HCN scenario, while the proportion is projected to decrease to 38.2% under the SCN scenario. Therefore, the gas-based DRI technology is required to offset the additional CO₂ emissions under the SCN scenario.

3.3. Industry Technology Adoptions and Interactions

To gain a deeper understanding of the connection and feedback between decarbonization strategies across various sectors, we analyzed the effects of the electrification process of four other sectors on the power industry’s electricity generation (see the Supporting Information for the interactions between the iron and steel industry and other sectors).

The results indicated that the decarbonization strategies implemented by other sectors would have a significant impact on the power industry. Specifically, power consumption is projected to be 4.7 PWh in 2060 under the SCN scenario, which is 3.06 times higher than the power consumption in 2020 (Figure 4). This significantly exceeds the current single-sector power demand forecast (42−44) (multiplied by five before comparison, see the Supporting Information for details).

Figure 4. Power consumption and industry contributions under the SCN scenario. The power consumption in 2020 is calculated by historical data.

The adoption of PPTs and conventional technologies are projected to contribute to 151% and 68% increases in power consumption, respectively, whereas AOTs are projected to result in a 13% reduction in power consumption. Note that the adoption of conventional technologies with CCS is projected to be one of the major drivers of this power consumption increase and projected to account for a 57% increase in power consumption. As this technology will be widely adopted in the future to meet global warming targets, the additional impacts of the CCS deployed, therefore, must be given serious consideration.

Among the industries, the road transport sector is projected to be the main driver of the increase in power consumption. Electric vehicles are projected to surpass fuel and diesel vehicles as the primary mode of transportation in 2040 and will contribute 66% of the power consumption increase in 2060. Additionally, the iron and steel industry and building sector are also expected to increase power consumption because of their electrification process. The share of electric arc furnace (EAF) steelmaking is projected to account for 25% of total crude steel productions by 2030 and will further contribute to 87.9% of the power consumption increase in the iron and steel industry by 2060. In 2016, the Chinese government implemented a “coal to electricity” project to reduce carbon emissions in the building sector. This process in the building sector is projected to accelerate in the future, with more than 40% of buildings projected to be fully electrified by 2060 and contributing 23% of the power consumption increase. However, the energy intensity of the building sector is projected to decline by adopting AOTs and result in a 10% reduction in power consumption by 2060.

3.4. Marginal Abatement Cost

Figure 5a shows the detailed carbon abatement potential and carbon abatement cost of each of the 111 CATs under the SCN scenarios and the marginal abatement cost curve for each industry. Thirteen CATs are discarded by the model because their abatement costs outweighed their abatement benefits or lacked cost competitiveness under the carbon abatement targets. Among these sectors, the power industry has the lowest marginal abatement cost at all carbon reduction ratios. It is followed by the cement, iron and steel, and building sectors. The marginal abatement cost for the road traffic sector is projected to be low in the initial period but increase rapidly thereafter, as the price of electric vehicles and fuel cell vehicles in taxis is already lower than that of conventional fuel vehicles but only accounts for 1.4% of the road traffic sector’s total carbon reductions in 2060.

Figure 5. Marginal abatement cost curve. a, Marginal abatement cost curve for industries under the SCN scenario. b, Marginal abatement cost under the SCN and HCN scenarios.

In the carbon neutral period, the marginal abatement costs for the power, cement, iron and steel, building, and road traffic sectors are 159, 372, 1919, 3642, and 4062 CNY/tCO₂, respectively (calculated based on the fitted curve when the reduction ratio is 0.95), and the marginal abatement cost for all five industries is 2189 CNY/tCO₂ under the SCN scenario, which is almost the same as the mean value reported by Zhang et al. (9) Interestingly, a more ambitious abatement strategy is projected to result in a lower marginal abatement cost (Figure 5b). The marginal abatement cost for the HCN scenario in the carbon neutral period is 1803 CNY/tCO₂, which is 18% lower than that for the SCN scenario. This is because a more ambitious carbon abatement strategy reduces the adoption of transitional technologies, which have a higher carbon abatement cost because of their lower carbon reduction efficiency (e.g., biomass power and gas-fired power). The widespread adoption of these transitional technologies in the SCN scenario, on the one hand, is projected to directly increase the marginal abatement cost. On the other hand, it can also increase the pressure to reduce emissions, as additional technologies are needed to offset the carbon emissions from transitional technologies.

3.5. Cost-Benefit Analysis

To understand the net economic effect of different carbon abatement strategies, we further conducted a cost-benefit analysis that used the social cost of carbon to reflect the abatement benefit and compared it with the carbon abatement cost (SCC). SCC is the monetized value of the damages to society caused by an incremental metric tonne of CO₂, which is equivalent to the benefit of each metric tonne of CO₂ avoided. It has been widely used as a central concept to assess a climate policy’s benefit. (45) In this study, we used the newest SCC assessment results reported by the Interagency Working Group on Social Cost of Greenhouse Gases (IWG), a group established by the U.S. government to update the SCC for policy cost-benefit analysis among different agencies as the metric to measure the benefit of decarbonization. The mean value of the SCC for 2020 was $51 (in 2020 dollars under a 3% discount rate) and is projected to increase annually at an average rate of 1.6%.

The results of the cost-benefit analysis reveal that the benefits of carbon reduction far outweigh their costs, and this gap is projected to widen over time under both the SCN and HCN scenarios (Figure 6). The cumulative costs of carbon reduction from 2021 to 2060 are 21.2 trillion and 26.0 trillion CNY under SCN and HCN, respectively, whereas the cumulative benefits of carbon reduction are 27.7 trillion and 33.8 trillion CNY, respectively, which are 6.5 and 7.8 trillion CNY higher than their costs. This result suggests that reducing carbon emissions in China would benefit China and the world and have more benefits if China enforced a more ambitious decarbonization strategy.

Figure 6. Annual carbon abatement costs and benefits from 2021 to 2060.

4. Policy Implication

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To augment its efforts toward carbon reduction, the Chinese government proposed a “1+N” policy framework for achieving carbon peaking and carbon neutrality in October 2021. The “1” in the framework denotes a long-term approach to combating climate change, while the “N” presents various solutions to achieving peak carbon emissions in key sectors, including energy, industry, construction, and transport. Furthermore, following 8 years of pilot programs, China established a national carbon trading market in July 2021, with the expectation of reducing carbon emissions through market-based mechanisms. To enhance the efficacy of climate policy and the carbon trading market, we derived the following lessons:

1.	When formulating decarbonization strategies for individual industries, it is essential to comprehensively consider the interaction impacts of multiple industries. This study revealed a significant connection and feedback mechanism between multiple sectors, with the electrification process of other sectors increasing the electricity demand by 206%, indicating a higher decarbonization pressure for the power industry. Therefore, we recommend integrating each sector’s solutions and considering the interaction impacts of each sector when formulating the “1+N” policy framework.
2.	Decarbonization strategies consistent with the Chinese reality should be developed. Natural gas has been regarded as the transitional energy to replace coal in meeting carbon reduction targets. However, the Chinese energy endowment is notable as “rich in coal but short of oil and gas”, and more than 40% of natural gas used by China comes from imports in recent years. The decarbonization pathways projected by this study revealed that coal-based DRI production had a lower carbon abatement cost than gas-based DRI production and can account for 45% of the iron and steel’s total carbon reduction under the HCN scenario. Therefore, China could adapt its resource endowment, taking coal as the foundation of economic development and developing coal-based technologies with CCS.
3.	China could consider using the price floor for the Chinese national emissions trading market when expanding it to include more industries. The average carbon trading price in the national carbon trading market (power industry only) was approximately 55 CNY in 2020. According to our projections, the marginal abatement cost to achieve carbon neutrality for the power industry is 159 CNY/tCO₂ and 2189 CNY/tCO₂ for all five sectors, significantly higher than the current trading price. Thus, a price floor for the Chinese national emission trading market may be necessary to maximize its effectiveness and achieve the carbon neutrality target.
4.	More ambitious decarbonization strategies may contribute to more benefits. Applying a more ambitious decarbonization pathway intuitively may increase the abatement cost. However, the projections suggested that a more ambitious decarbonization could result in a lower marginal abatement cost and higher net carbon abatement benefit. Therefore, China could consider strengthening its carbon reduction efforts and enforcing a more ambitious carbon mitigation strategy to achieve its carbon reduction goals.

5. Discussion

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In this study, we first established the CNTD platform to support the development of the “bottom-up” energy and climate integrated assessment models. Based on the platform, we then developed the MESSAGEix-CAEP model and analyzed the decarbonization pathways for each industry, as well as their interactions. Finally, we revealed the marginal abatement cost curve for industries and conducted a cost-benefit analysis under the SCN and HCN scenarios.

Our findings indicated that the connections and feedback between sectors should not be disregarded. The electrification process of other sectors results in a 206% increase in electricity demand, highlighting the need for integrated solutions for each sector. Additionally, we found that the marginal abatement cost to achieve carbon neutrality across all five industries was 2189 CNY/tCO₂, significantly higher than the current China national carbon trading price. But it is projected to decrease when implementing a more ambitious decarbonization strategy. On the basis of these results, we suggest the Chinese government should strengthen its carbon reduction efforts and increase the trading price of the current national carbon trading market to motivate carbon reduction in the early stage.

This study also has some limitations that may inform future research needs. First, the model used in this study is based on the five Chinese energy-intensive sectors, and more sectors should be incorporated into the MESSAGE-CAEP model to provide a comprehensive understanding of Chinese decarbonization pathways and their costs and benefits. Second, although the social cost of carbon has been widely used as a standard measure to evaluate the benefit of a climate policy, it is subject to large uncertainties stemming from different model parameters and boundary designs. Therefore, a more detailed benefit analysis is needed to align with the Chinese decarbonization strategy and to provide decomposition benefits, such as those from health and biodiversity improvements. Finally, as a simplification, we assume that carbon sequestration will lead to a reduction of 10% in 2020 carbon emissions by 2060, as reported in the literature. However, given the ongoing research on natural-based and natural process-oriented solutions such as afforestation, reforestation, and land-use, land-use change and forestry (LULUCF), it is necessary to incorporate specific carbon sequestration technologies into this model to accurately assess the contributions of carbon sequestration.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.2c08171.

Details description of the CNTD platform, the database for each sector, supplemental methods for marginal abatement cost, sensitive analysis for different vehicle ownership saturation levels, and additional results regarding the decarbonization pathways, energy consumption, interaction between sectors, and marginal abatement cost under HCN scenario (PDF)

es2c08171_si_001.pdf (1.44 MB)

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Author Information

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Corresponding Authors
- Shaohui Zhang - School of Economics and Management, Beihang University, Beijing 100191, China; International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria; https://orcid.org/0000-0003-2487-8574; Email: [email protected]
- Jinnan Wang - State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China; Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China; Email: [email protected]
Authors
- Jinchi Dong - State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China; https://orcid.org/0000-0001-8869-8818
- Bofeng Cai - Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
- Hui Yue - Center for Energy, Environment & Economy Research, School of Management, Zhengzhou University, Zhengzhou 450001, China; Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, Netherlands
- Can Wang - State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China; https://orcid.org/0000-0002-1136-792X
- Xianqiang Mao - School of Environment, Beijing Normal University, Beijing 100875, China; https://orcid.org/0000-0001-5924-8180
- Jianhui Cong - School of Economics and Management, Shanxi University, Taiyuan 030000, China
- Fei Guo - International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
Author Contributions
J.D. and B.C. contributed equally to this work.
Notes
The authors declare no competing financial interest.

Acknowledgments

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This work was supported by the China Scholarship Council (202206190157), Central University Excellent Youth Team Project, the Fine Particle Research Initiative in East Asia Considering National Differences (FRIEND) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Project No. 2020M3G1A1114622), the Korea Environment Industry & Technology Institute (KEITI) through the Climate Change R&D Project for New Climate Regime, funded by the Korea Ministry of Environment (MOE) (2022003560007), and the National Natural Science Foundation of China (71904007, 72140004, 72074154).

References

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This article references 45 other publications.

1
IPCC WGI AR6. Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., Zhou, B., Eds.; Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA, 2021; pp 3– 32. https://www.ipcc.ch/report/ar6/wg1/Chapter/summary-for-policymakers/ (accessed 2023-02-25).
Google Scholar
There is no corresponding record for this reference.
2
Shan, Y.; Guan, D.; Zheng, H.; Ou, J.; Li, Y.; Meng, J.; Mi, Z.; Liu, Z.; Zhang, Q. China CO2 emission accounts 1997–2015. Scientific Data 2018, 5 (1), 170201, DOI: 10.1038/sdata.2017.201
Google Scholar
2
China CO2 emission accounts 1997-2015
Shan, Yuli; Guan, Dabo; Zheng, Heran; Ou, Jiamin; Li, Yuan; Meng, Jing; Mi, Zhifu; Liu, Zhu; Zhang, Qiang
Scientific Data (2018), 5 (1), 170201CODEN: SDCABS; ISSN:2052-4463. (Nature Research)
China is the world's top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China's CO2 emissions is the first step in implementing redn. policies. However, no annual, officially published emissions data exist for China. The current emissions estd. by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions ests. is approx. equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement prodn.). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions redn. policy-making in China.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXovFSlug%253D%253D&md5=803fff0b4541593de7ee44d11bdd6665
3
Zheng, X.; Lu, Y.; Yuan, J.; Baninla, Y.; Zhang, S.; Stenseth, N. C.; Hessen, D. O.; Tian, H.; Obersteiner, M.; Chen, D. Drivers of change in China’s energy-related CO2 emissions. Proc. Natl. Acad. Sci. U. S. A. 2020, 117 (1), 29– 36, DOI: 10.1073/pnas.1908513117
Google Scholar
3
Drivers of change in China's energy-related CO2 emissions
Zheng, Xiaoqi; Lu, Yonglong; Yuan, Jingjing; Baninla, Yvette; Zhang, Sheng; Stenseth, Nils Chr.; Hessen, Dag O.; Tian, Hanqin; Obersteiner, Michael; Chen, Deliang
Proceedings of the National Academy of Sciences of the United States of America (2020), 117 (1), 29-36CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
A review. CO2 emissions are of global concern because of climate change. China has become the largest CO2 emitter in the world and presently accounts for 30% of global emissions. Here, we analyze the major drivers of energy-related CO2 emissions in China from 1978 when the reform and opening-up policy was launched. We find that (1) there was a 6-fold increase in energy-related CO2 emissions, which was driven primarily (176%) by economic growth followed by population growth (16%), while the effects of energy intensity (-79%) and carbon intensity (-13%) slowed the growth of carbon emissions over most of this period; (2) energy-related CO2 emissions are pos. related to per capita gross domestic product (GDP), population growth rate, carbon intensity, and energy intensity; and (3) a portfolio of command-and-control policies affecting the drivers has altered the total emission trend. However, given the major role of China in global climate change mitigation, significant future redns. in China's CO2 emissions will require transformation toward low-carbon energy systems.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmtFKrsg%253D%253D&md5=17ad78adbe055473ef14c78ea2fead20
4
Guan, D.; Meng, J.; Reiner, D. M.; Zhang, N.; Shan, Y.; Mi, Z.; Shao, S.; Liu, Z.; Zhang, Q.; Davis, S. J. Structural decline in China’s CO2 emissions through transitions in industry and energy systems. Nature Geoscience 2018, 11 (8), 551– 555, DOI: 10.1038/s41561-018-0161-1
Google Scholar
4
Structural decline in China's CO2 emissions through transitions in industry and energy systems
Guan, Dabo; Meng, Jing; Reiner, David M.; Zhang, Ning; Shan, Yuli; Mi, Zhifu; Shao, Shuai; Liu, Zhu; Zhang, Qiang; Davis, Steven J.
Nature Geoscience (2018), 11 (8), 551-555CODEN: NGAEBU; ISSN:1752-0894. (Nature Research)
As part of the Paris Agreement, China pledged to peak its CO2 emissions by 2030. In retrospect, the commitment may have been fulfilled as it was being made-China's emissions peaked in 2013 at a level of 9.53 gigatons of CO2, and have declined in each year from 2014 to 2016. However, the prospect of maintaining the continuance of these redns. depends on the relative contributions of different changes in China. Here, we quant. evaluate the drivers of the peak and decline of China's CO2 emissions between 2007 and 2016 using the latest available energy, economic and industry data. We find that slowing economic growth in China has made it easier to reduce emissions. Nevertheless, the decline is largely assocd. with changes in industrial structure and a decline in the share of coal used for energy. Decreasing energy intensity (energy per unit gross domestic product) and emissions intensity (emissions per unit energy) also contributed to the decline. Based on an econometric (cumulative sum) test, we confirm that there is a clear structural break in China's emission pattern around 2015. We conclude that the decline of Chinese emissions is structural and is likely to be sustained if the nascent industrial and energy system transitions continue.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXht1OjsrvL&md5=bbf19d393bb088487d63050b3a4ca8b5
5
Rosa, E. A.; Dietz, T. Human drivers of national greenhouse-gas emissions. Nature Climate Change 2012, 2 (8), 581– 586, DOI: 10.1038/nclimate1506
Google Scholar
5
Human drivers of national greenhouse-gas emissions
Rosa, Eugene A.; Dietz, Thomas T.
Nature Climate Change (2012), 2 (8), 581-586CODEN: NCCACZ; ISSN:1758-6798. (Nature Publishing Group)
A review. Centuries of speculation about the causes of human stress on the environment is now being disciplined with empirical evidence, including analyses of differences in greenhouse-gas emissions across contemporary nation states. The cumulative results can provide useful guidance for both climate projections and for policy design. Growing human population and affluence clearly contribute to enhanced environmental stress. Evidence does not support the argument for amelioration of greenhouse-gas emissions at the highest levels of affluence. However, the role of other factors, such as urbanization, trade, culture and institutions remains ambiguous.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtV2isLvF&md5=4ae2cf24946f90f9cda1b2ab28010689
6
Cai, B.; Cao, L.; Lei, Y.; Wang, C.; Zhang, L.; Zhu, J.; Li, M.; Du, M.; Lv, C.; Jiang, H.; Ning, M.; Wang, J. China’s carbon emission pathway under the carbon neutrality target. China Population,Resources and Environment 2021, 31 (01), 7– 14
Google Scholar
There is no corresponding record for this reference.
7
Li, W.; Zhang, S.; Lu, C. Research on the driving factors and carbon emission reduction pathways of China’s iron and steel industry under the vision of carbon neutrality. Journal of Cleaner Production 2022, 361, 132237 DOI: 10.1016/j.jclepro.2022.132237
Google Scholar
7
Research on the driving factors and carbon emission reduction pathways of China's iron and steel industry under the vision of carbon neutrality
Li, Wei; Zhang, Shuohua; Lu, Can
Journal of Cleaner Production (2022), 361 (), 132237CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
Under the vision of carbon neutrality, China's iron and steel industry (CISI) urgently needs to achieve low-carbon development. To formulate effective and targeted emission redn. policies for CISI, the driving forces of carbon dioxide (CO2) emissions and future emission redn. pathways in CISI are explored in this paper. The Logarithmic Mean Divisia Index (LMDI) method and the Mean Impact Value (MIV) technique are adopted to analyze the driving factors of CO2 emissions in CISI at historical and prospective dimensions, resp. Furthermore, the extreme learning machine (ELM) model optimized by the bat algorithm (BA) is established to project the carbon emission redn. pathways of CISI during 2020-2050 under the business-as-usual (BAU) scenario, the low-speed, medium-speed, and high-speed development scenarios considering the constraint of the carbon neutrality target. The results reveal that prodn. capacity and energy efficiency are essential drivers of CO2 emissions in CISI. Consequently, aimed at achieving carbon neutrality, CISI should focus on eliminating backward capacity and simultaneously accelerating the deployment of advanced technologies. Addnl., it is difficult to accomplish the carbon neutrality goal by 2060 under the BAU scenario. Conversely, under the optimal emission redn. pathway detd. by the high-speed development scenario, CISI will reach its peak in 2022 with a peak value of 2143.42 million tons of CO2 (MtCO2). The av. annual emission abatement rate during 2022-2050 is maintained at approx. 4.47% and the cumulative redn. rate in 2050 will exceed 70% compared to the base year 2019. CISI is required to develop more stringent emission redn. measures to achieve significant emission abatement. The crude steel prodn. capacity should be reduced to 533 Mt in 2050 and the capacity utilization rate should be maintained beyond 80%. The energy consumption per ton of steel must be decreased to 264 Kg of coal equiv. (Kgce) in 2050.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhvFWhsb3F&md5=9d06e0cbfe0adf45f056e7dfc9f10b5b
8
Wang, H.; Lu, X.; Deng, Y.; Sun, Y.; Nielsen, C. P.; Liu, Y.; Zhu, G.; Bu, M.; Bi, J.; McElroy, M. B. China’s CO2 peak before 2030 implied from characteristics and growth of cities. Nature Sustainability 2019, 2 (8), 748– 754, DOI: 10.1038/s41893-019-0339-6
Google Scholar
There is no corresponding record for this reference.
9
Zhang, S.; Chen, W. Assessing the energy transition in China towards carbon neutrality with a probabilistic framework. Nature. Communications 2022, 13 (1), 87, DOI: 10.1038/s41467-021-27671-0
Google Scholar
9
Assessing the energy transition in China towards carbon neutrality with a probabilistic framework
Zhang, Shu; Chen, Wenying
Nature Communications (2022), 13 (1), 87CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)
A profound transformation of China's energy system is required to achieve carbon neutrality. Here, we couple Monte Carlo anal. with a bottom-up energy-environment-economy model to generate 3,000 cases with different carbon peak times, technol. evolution pathways and cumulative carbon budgets. The results show that if emissions peak in 2025, the carbon neutrality goal calls for a 45-62% electrification rate, 47-78% renewable energy in primary energy supply, 5.2-7.9 TW of solar and wind power, 1.5-2.7 PWh of energy storage usage and 64-1,649 MtCO2 of neg. emissions, and synergistically reducing approx. 80% of local air pollutants compared to the present level in 2050. The emission peak time and cumulative carbon budget have significant impacts on the decarbonization pathways, technol. choices, and transition costs. Early peaking reduces welfare losses and prevents overreliance on carbon removal technologies. Technol. breakthroughs, prodn. and consumption pattern changes, and policy enhancement are urgently required to achieve carbon neutrality.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xns1Ghug%253D%253D&md5=00160dcb7aaf66f80cb7f5a6dbfaf135
10
Langevin, J.; Harris, C. B.; Reyna, J. L. Assessing the Potential to Reduce U.S. Building CO2 Emissions 80% by 2050. Joule 2019, 3 (10), 2403– 2424, DOI: 10.1016/j.joule.2019.07.013
Google Scholar
There is no corresponding record for this reference.
11
Zhou, S.; Tong, Q.; Pan, X.; Cao, M.; Wang, H.; Gao, J.; Ou, X. Research on low-carbon energy transformation of China necessary to achieve the Paris agreement goals: A global perspective. Energy Economics 2021, 95, 105137 DOI: 10.1016/j.eneco.2021.105137
Google Scholar
There is no corresponding record for this reference.
12
van Sluisveld, M. A. E.; de Boer, H. S.; Daioglou, V.; Hof, A. F.; van Vuuren, D. P. A race to zero - Assessing the position of heavy industry in a global net-zero CO2 emissions context. Energy and Climate Change 2021, 2, 100051 DOI: 10.1016/j.egycc.2021.100051
Google Scholar
12
A race to zero - Assessing the position of heavy industry in a global net-zero CO2 emissions context
van Sluisveld, Mariesse A. E.; de Boer, Harmen Sytze; Daioglou, Vassilis; Hof, Andries F.; van Vuuren, Detlef P.
Energy and Climate Change (2021), 2 (), 100051CODEN: ECCNA7; ISSN:2666-2787. (Elsevier B.V.)
In this study, we explore the decarbonisation pathways of four carbon and energy-intensive industries (resp. iron & steel, clinker & cement, chems. and pulp & paper) in the context of a global 2050 net-zero carbon emissions objective using the IMAGE integrated assessment model. We systematically test the robustness of the model by studying its responses to four different decarbonisation narratives and across six different world regions. The study underpins earlier conclusions in the literature on 'residual emissions' and 'hard-to-abate sectors', such as the persistence of residual emissions and the overall continued use of fossil fuels by heavy industries within the global 2050 net-zero context (with the pulp & paper sector as an exception). However, under the condition that net-neg. emissions are achieved in the power and energy conversion sectors prior to the 2050 landmark, the indirect emission removals can compensate for the residual emissions left in the industry sectors, rendering these sectors 'net-zero' as early as the 2040s. Full decarbonisation of industrial (sub)sector(s) is found to be possible, but only under very specific narratives and likely outside of the 2050 timeline for the iron & steel, clinker & cement and the chem. sector. Subsequently, we find that the decarbonisation patterns in IMAGE are industry and regionally specific, though, different strategic considerations (narratives) did not substantially change the models' decarbonisation response before or after 2050. Important aspects of the decarbonisation responses are the (direct and indirect) electrification of the iron & steel sector, a full dependency on carbon removal technologies in the clinker & cement sector, the closing of carbon and material loops in the chem. sector and zero-carbon heating for the pulp & paper sector. However, further research and modeling efforts are needed to study a broader palette of conceivable decarbonisation pathways and implications for industry within a global 2050 net-zero economy context.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhtlWltrvL&md5=a3539d9ff66528309125ae0527092934
13
Duan, H.; Zhou, S.; Jiang, K.; Bertram, C.; Harmsen, M.; Kriegler, E.; van Vuuren, D. P.; Wang, S.; Fujimori, S.; Tavoni, M.; Ming, X.; Keramidas, K.; Iyer, G.; Edmonds, J. Assessing China’s efforts to pursue the 1.5°C warming limit. Science 2021, 372 (6540), 378– 385, DOI: 10.1126/science.aba8767
Google Scholar
13
Assessing China's efforts to pursue the 1.5°C warming limit
Duan, Hongbo; Zhou, Sheng; Jiang, Kejun; Bertram, Christoph; Harmsen, Mathijs; Kriegler, Elmar; van Vuuren, Detlef P.; Wang, Shouyang; Fujimori, Shinichiro; Tavoni, Massimo; Ming, Xi; Keramidas, Kimon; Iyer, Gokul; Edmonds, James
Science (Washington, DC, United States) (2021), 372 (6540), 378-385CODEN: SCIEAS; ISSN:1095-9203. (American Association for the Advancement of Science)
Given the increasing interest in keeping global warming below 1.5°C, a key question is what this would mean for China's emission pathway, energy restructuring, and decarbonization. By conducting a multimodel study, we find that the 1.5°C-consistent goal would require China to reduce its carbon emissions and energy consumption by more than 90 and 39%, resp., compared with the "no policy" case. Neg. emission technologies play an important role in achieving near-zero emissions, with captured carbon accounting on av. for 20% of the total redns. in 2050. Our multimodel comparisons reveal large differences in necessary emission redns. across sectors, whereas what is consistent is that the power sector is required to achieve full decarbonization by 2050. The cross-model avs. indicate that China's accumulated policy costs may amt. to 2.8 to 5.7% of its gross domestic product by 2050, given the 1.5°C warming limit.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXpvFajt78%253D&md5=f5cccf5df88a29de8884489a2b3b0b2b
14
Cao, J.; Dai, H.; Li, S.; Guo, C.; Ho, M.; Cai, W.; He, J.; Huang, H.; Li, J.; Liu, Y.; Qian, H.; Wang, C.; Wu, L.; Zhang, X. The general equilibrium impacts of carbon tax policy in China: A multi-model comparison. Energy Economics 2021, 99, 105284 DOI: 10.1016/j.eneco.2021.105284
Google Scholar
There is no corresponding record for this reference.
15
Zhao, X.-g.; Jiang, G.-w.; Nie, D.; Chen, H. How to improve the market efficiency of carbon trading: A perspective of China. Renewable and Sustainable Energy Reviews 2016, 59, 1229– 1245, DOI: 10.1016/j.rser.2016.01.052
Google Scholar
There is no corresponding record for this reference.
16
McPhearson, T.; M. Raymond, C.; Gulsrud, N.; Albert, C.; Coles, N.; Fagerholm, N.; Nagatsu, M.; Olafsson, A. S.; Soininen, N.; Vierikko, K. Radical changes are needed for transformations to a good Anthropocene. npj Urban Sustainability 2021, 1 (1), 5, DOI: 10.1038/s42949-021-00017-x
Google Scholar
There is no corresponding record for this reference.
17
IEA. Going carbon negative: What are the technology options? ; 2020. https://www.iea.org/commentaries/going-carbon-negative-what-are-the-technology-options (accessed 2023-02-25).
Google Scholar
There is no corresponding record for this reference.
18
IPCC WGI AR6. Summary for Policymakers. In Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Shukla, P. R., Skea, J., Slade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., Belkacemi, M.; Hasija, A., Lisboa, G., Luz, S., Malley, J., Eds.; Cambridge University Press: Cambridge, UK and New York, NY, USA, 2022. https://www.ipcc.ch/report/ar6/wg3/ (accessed 2023-02-25).
Google Scholar
There is no corresponding record for this reference.
19
IEA. Net Zero by 2050; IEA, Paris, License: CC BY 4.0; 2021. https://www.iea.org/reports/net-zero-by-2050 (accessed 2023-02-25).
Google Scholar
There is no corresponding record for this reference.
20
Huppmann, D.; Gidden, M.; Fricko, O.; Kolp, P.; Orthofer, C.; Pimmer, M.; Kushin, N.; Vinca, A.; Mastrucci, A.; Riahi, K.; Krey, V. The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. Environmental Modelling & Software 2019, 112, 143– 156, DOI: 10.1016/j.envsoft.2018.11.012
Google Scholar
There is no corresponding record for this reference.
21
Zhang, S.; Yi, B.; Guo, F.; Zhu, P. Exploring selected pathways to low and zero CO2 emissions in China’s iron and steel industry and their impacts on resources and energy. Journal of Cleaner Production 2022, 340, 130813 DOI: 10.1016/j.jclepro.2022.130813
Google Scholar
21
Exploring selected pathways to low and zero CO2 emissions in China's iron and steel industry and their impacts on resources and energy
Zhang, Shaohui; Yi, Bowen; Guo, Fei; Zhu, Pengyu
Journal of Cleaner Production (2022), 340 (), 130813CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
The increasing energy and material consumption assocd. with global economic growth has resulted in the need for more severe efforts at mitigating global climate change. The iron and steel industry consumes 8% of energy and emits 7% of total CO2 globally. China's iron and steel industry contributes to 15% of that country's total CO2 emissions. Therefore, there is an urgent need to explore the possibility of net zero emissions in the iron and steel industry in China to meet China's goal of carbon neutrality before 2060. In the study presented in this paper, the MESSAGEix-China iron and steel model was developed by integrating the process-based technol. of the sector into the IIASA's MESSAGEix framework to explore zero CO2 emission pathways and their assocd. impacts on resources, energy, and water in China's iron and steel industry up to 2100. We found that there are multiple pathways to achieving zero CO2 emissions in the Chinese iron and steel industry by the end of the 21st century. More specifically, in all the pathways developed in this study, CO2emissions decreased significantly between 2030 and 2060 due to the rapid application of 100% scrap-based Elec. Arc Furnaces (EAFs) and hydrogen-based Direct Reduced Iron (DRI)-EAFs steel-making technologies. However, by 2060, there will still be 70-360 Mt of CO2 emissions from China's iron and steel industry; consequently, carbon sink or neg. emission technologies are required to offset this and achieve the country's carbon neutrality goal. Furthermore, technologies for achieving zero emissions differ widely in terms of their impacts on the consumption of materials and energy. Compared to the elec. (ELE) scenarios, 25-40% of extra iron ore is consumed in the current and new national policy (NPS) scenarios and the DRI scenarios, but 25-220% of scrap is required. At the same time, 20-150% more energy will be saved in the ELE scenarios than in the NPS and DRI scenarios. Finally, we recommend that policy makers design a cross-cutting strategy to achieve zero CO2 emissions and enhance efforts for material recycling and the provision of clean energy and water.
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Dargay, J.; Gately, D.; Sommer, M. Vehicle ownership and income growth, worldwide: 1960–2030. energy journal 2007, 28 (4), 143, DOI: 10.5547/ISSN0195-6574-EJ-Vol28-No4-7
Google Scholar
There is no corresponding record for this reference.
23
Medlock, K. B.; Soligo, R. Car ownership and economic development with forecasts to the year 2015. Journal of Transport Economics and Policy 2002, 36 (2), 163– 188
Google Scholar
There is no corresponding record for this reference.
24
Wu, T.; Zhao, H.; Ou, X. Vehicle Ownership Analysis Based on GDP per Capita in China: 1963–2050. Sustainability 2014, 6 (8), 4877– 4899, DOI: 10.3390/su6084877
Google Scholar
There is no corresponding record for this reference.
25
Dargay, J.; Gately, D. Income’s effect on car and vehicle ownership, worldwide: 1960–2015. Transportation Research Part A: Policy and Practice 1999, 33 (2), 101– 138, DOI: 10.1016/S0965-8564(98)00026-3
Google Scholar
There is no corresponding record for this reference.
26
Huo, H.; Wang, M. Modeling future vehicle sales and stock in China. Energy Policy 2012, 43, 17– 29, DOI: 10.1016/j.enpol.2011.09.063
Google Scholar
There is no corresponding record for this reference.
27
Tan, M.; Li, X.; Xie, H.; Lu, C. Urban land expansion and arable land loss in China─a case study of Beijing–Tianjin–Hebei region. Land Use Policy 2005, 22 (3), 187– 196, DOI: 10.1016/j.landusepol.2004.03.003
Google Scholar
There is no corresponding record for this reference.
28
Gong, T.; Zhang, W.; Liang, J.; Lin, C.; Mao, K. Forecast and Analysis of the Total Amount of Civil Buildings in China in the Future Based on Population Driven. Sustainability 2021, 13 (24), 14051, DOI: 10.3390/su132414051
Google Scholar
There is no corresponding record for this reference.
29
Huo, T.; Xu, L.; Feng, W.; Cai, W.; Liu, B. Dynamic scenario simulations of carbon emission peak in China’s city-scale urban residential building sector through 2050. Energy Policy 2021, 159, 112612 DOI: 10.1016/j.enpol.2021.112612
Google Scholar
29
Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050
Huo, Tengfei; Xu, Linbo; Feng, Wei; Cai, Weiguang; Liu, Bingsheng
Energy Policy (2021), 159 (), 112612CODEN: ENPYAC; ISSN:0301-4215. (Elsevier Ltd.)
Understanding future trajectory of urban residential building carbon emissions (URBCE) is essential to seeking effective carbon-abatement pathways to combat climate change. However, future evolutionary trajectory, possible emission peaks and peaking times in this sector are still unclear. This study innovatively develops an integrated dynamic simulation model by embedding a bottom-up building end-use energy model into the system dynamics model. Based on this, scenario anal. approach is combined with Monte Carlo simulation method to explore the possible emission peaks and peaking times considering the uncertainties of impact factors. We apply the integrated SD-LEAP model to Chongqing, a typical city in Chinas hot-summer and cold-winter zone. Results show that URBCE will probably peak at 22.8 (±8.0) Mt CO2 in 2042 (±3.4)-well beyond Chinas 2030 target. Different building end-uses present substantial disparities. The contribution of combined heating and cooling to URBCE mitigation will be over 60% between business-as-usual and low-carbon scenarios. Dynamic sensitivity anal. reveals that per capita gross domestic product, carbon emission factor, and residential floor space per capita can boost emission peaks and peaking time. This study can not only boost the theory and model development for carbon emission prediction, but also assist governments to set effective carbon-redn. targets and policies.
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O’Neill, B. C.; Carter, T. R.; Ebi, K.; Harrison, P. A.; Kemp-Benedict, E.; Kok, K.; Kriegler, E.; Preston, B. L.; Riahi, K.; Sillmann, J.; van Ruijven, B. J.; van Vuuren, D.; Carlisle, D.; Conde, C.; Fuglestvedt, J.; Green, C.; Hasegawa, T.; Leininger, J.; Monteith, S.; Pichs-Madruga, R. Achievements and needs for the climate change scenario framework. Nature Climate Change 2020, 10 (12), 1074– 1084, DOI: 10.1038/s41558-020-00952-0
Google Scholar
30
Achievements and needs for the climate change scenario framework
O'Neill Brian C; Carlisle David; Green Carole; O'Neill Brian C; Carter Timothy R; Ebi Kristie; Harrison Paula A; Kemp-Benedict Eric; Kok Kasper; Kriegler Elmar; Preston Benjamin L; Riahi Keywan; van Ruijven Bas J; Sillmann Jana; Fuglestvedt Jan; van Vuuren Detlef; van Vuuren Detlef; Conde Cecilia; Hasegawa Tomoko; Leininger Julia; Monteith Seth; Pichs-Madruga Ramon
Nature climate change (2020), 10 (12), 1074-1084 ISSN:1758-678X.
Long-term global scenarios have underpinned research and assessment of global environmental change for four decades. Over the past ten years, the climate change research community has developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. Here we assess how well this framework is working and what challenges it faces. We synthesize insights from scenario-based literature, community discussions and recent experience in assessments, concluding that the framework has been widely adopted across research communities and is largely meeting immediate needs. However, some mixed successes and a changing policy and research landscape present key challenges, and we recommend several new directions for the development and use of this framework.
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O’Neill, B. C.; Kriegler, E.; Ebi, K. L.; Kemp-Benedict, E.; Riahi, K.; Rothman, D. S.; van Ruijven, B. J.; van Vuuren, D. P.; Birkmann, J.; Kok, K. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global environmental change 2017, 42, 169– 180, DOI: 10.1016/j.gloenvcha.2015.01.004
Google Scholar
There is no corresponding record for this reference.
32
Chen, Y.; Guo, F.; Wang, J.; Cai, W.; Wang, C.; Wang, K. Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. Scientific Data 2020, 7 (1), 1– 13, DOI: 10.1038/s41597-020-0421-y
Google Scholar
There is no corresponding record for this reference.
33
McKitrick, R. A Derivation of the Marginal Abatement Cost Curve. Journal of Environmental Economics and Management 1999, 37 (3), 306– 314, DOI: 10.1006/jeem.1999.1065
Google Scholar
There is no corresponding record for this reference.
34
Kesicki, F.; Strachan, N. Marginal abatement cost (MAC) curves: confronting theory and practice. Environmental Science & Policy 2011, 14 (8), 1195– 1204, DOI: 10.1016/j.envsci.2011.08.004
Google Scholar
There is no corresponding record for this reference.
35
DECC, D. f. E. a. C. C. Carbon Valuation in UK Policy Appraisal: A Revised Approach ; 2009.
Google Scholar
There is no corresponding record for this reference.
36
Kesicki, F. Marginal abatement cost curves for policy making–expert-based vs. model-derived curves ; 2010. https://www.homepages.ucl.ac.uk/~ucft347/Kesicki_MACC.pdf (accessed 2023-02-25).
Google Scholar
There is no corresponding record for this reference.
37
Du, L.; Hanley, A.; Wei, C. Estimating the Marginal Abatement Cost Curve of CO2 Emissions in China: Provincial Panel Data Analysis. Energy Economics 2015, 48, 217– 229, DOI: 10.1016/j.eneco.2015.01.007
Google Scholar
There is no corresponding record for this reference.
38
Ma, C.; Hailu, A.; You, C. A critical review of distance function based economic research on China’s marginal abatement cost of carbon dioxide emissions. Energy Economics 2019, 84, 104533 DOI: 10.1016/j.eneco.2019.104533
Google Scholar
There is no corresponding record for this reference.
39
Xiao, H.; Wei, Q.; Wang, H. Marginal abatement cost and carbon reduction potential outlook of key energy efficiency technologies in China′s building sector to 2030. Energy Policy 2014, 69, 92– 105, DOI: 10.1016/j.enpol.2014.02.021
Google Scholar
There is no corresponding record for this reference.
40
Cai, W.; He, N.; Li, M.; Xu, L.; Wang, L.; Zhu, J.; Zeng, N.; Yan, P.; Si, G.; Zhang, X.; Cen, X.; Yu, G.; Sun, O. J. Carbon sequestration of Chinese forests from 2010 to 2060: spatiotemporal dynamics and its regulatory strategies. Science Bulletin 2022, 67 (8), 836– 843, DOI: 10.1016/j.scib.2021.12.012
Google Scholar
40
Carbon sequestration of Chinese forests from 2010 to 2060: spatiotemporal dynamics and its regulatory strategies
Cai, Weixiang; He, Nianpeng; Li, Mingxu; Xu, Li; Wang, Longzhu; Zhu, Jianhua; Zeng, Nan; Yan, Pu; Si, Guoxin; Zhang, Xiaoquan; Cen, Xiaoyu; Yu, Guirui; Sun, Osbert Jianxin
Science Bulletin (2022), 67 (8), 836-843CODEN: SBCUA5; ISSN:2095-9281. (Elsevier B.V.)
Forestation is important for sequestering atm. carbon, and it is a cost-effective and nature-based soln. (NBS) for mitigating global climate change. Here, under the assumption of forestation in the potential plantable lands, we used the forest carbon sequestration (FCS) model and field survey involving 3365 forest plots to assess the carbon sequestration rate (CSR) of Chinese existing and new forestation forests from 2010 to 2060 under three forestation and three climate scenarios. Without considering the influence of extreme events and human disturbance, the estd. av. CSR in Chinese forests was 0.358 ± 0.016 Pg C a-1, with partitioning to biomass (0.211 ± 0.016 Pg C a-1) and soil (0.147 ± 0.005 Pg C a-1), resp. The existing forests account for approx. 93.5% of the CSR, which will peak near 2035, and decreasing trend was present overall after 2035. After 2035, effective tending management is required to maintain the high CSR level, such as selective cutting, thinning, and approx. disturbance. However, new forestation from 2015 in the potential plantable lands would play a minimal role in addnl. CSR increases. In China, the CSR is generally higher in the Northeast, Southwest, and Central-South, and lower in the Northwest. Considering the potential losses through deforestation and logging, it is realistically estd. that CSR in Chinese forests would remain in the range of 0.161-0.358 Pg C a-1 from 2010 to 2060. Overall, forests have the potential to offset 14.1% of the national anthropogenic carbon emissions in China over the period of 2010-2060, significantly contributing to the carbon neutrality target of 2060 with the implementation of effective management strategies for existing forests and expansion of forestation.
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Huang, Y.; Sun, W.; Qin, Z.; Zhang, W.; Yu, Y.; Li, T.; Zhang, Q.; Wang, G.; Yu, L.; Wang, Y.; Ding, F.; Zhang, P. The role of China’s terrestrial carbon sequestration 2010–2060 in offsetting energy-related CO2 emissions. National Science Review 2022, 9 (8), nwac057 DOI: 10.1093/nsr/nwac057
Google Scholar
There is no corresponding record for this reference.
42
Zhuo, Z.; Du, E.; Zhang, N.; Nielsen, C. P.; Lu, X.; Xiao, J.; Wu, J.; Kang, C. Cost increase in the electricity supply to achieve carbon neutrality in China. Nature. Communications 2022, 13 (1), 3172, DOI: 10.1038/s41467-022-30747-0
Google Scholar
42
Cost increase in the electricity supply to achieve carbon neutrality in China
Zhuo, Zhenyu; Du, Ershun; Zhang, Ning; Nielsen, Chris P.; Lu, Xi; Xiao, Jinyu; Wu, Jiawei; Kang, Chongqing
Nature Communications (2022), 13 (1), 3172CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)
Abstr.: The Chinese government has set long-term carbon neutrality and renewable energy (RE) development goals for the power sector. Despite a precipitous decline in the costs of RE technologies, the external costs of renewable intermittency and the massive investments in new RE capacities would increase electricity costs. Here, we develop a power system expansion model to comprehensively evaluate changes in the electricity supply costs over a 30-yr transition to carbon neutrality. RE supply curves, operating security constraints, and the characteristics of various generation units are modelled in detail to assess the cost variations accurately. According to our results, approx. 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in the power system by 2050. The electricity supply costs would increase by 9.6 CNY¢/kWh. The major cost shift would result from the substantial investments in RE capacities, flexible generation resources, and network expansion.
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Wu, G.; Niu, D. A study of carbon peaking and carbon neutral pathways in China’s power sector under a 1.5 °C temperature control target. Environmental Science and Pollution Research 2022, 29 (56), 85062– 85080, DOI: 10.1007/s11356-022-21594-z
Google Scholar
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A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5 °C temperature control target
Wu Gengqi; Niu Dongxiao; Wu Gengqi; Niu Dongxiao
Environmental science and pollution research international (2022), 29 (56), 85062-85080 ISSN:.
The clean and low-carbon transition of China's power sector is of great importance to the achievement of dual carbon targets and the control of global warming. This paper first estimates the remaining carbon budget of the power sector under a 1.5 °C temperature control target and on this basis constructs 1.5 °C and 2 °C power transition scenarios, examining key boundary conditions such as economic development and changes in the cost of power generation technologies. Second, the Genetic Algorithm-Extreme Learning Machine (GA-ELM) model is used to forecast the electricity demand for the next forty years. Finally, with the objective of minimising the total planning cost, a pathway optimisation model of the power system is constructed to explore the optimal transition path for the power system using the dual carbon target, carbon budget and electricity demand as the main constraints. The results of the study show that the carbon budget of the Chinese power sector is approximately 7.1 × 10(10) t CO2 for a 1.5 °C temperature control target. The electricity demand tends to saturate after 2050 and reaches 1.58 × 10(13) kWh in 2060. The time of the carbon peak and carbon neutralisation in the power sector is 5 years ahead of the double carbon target. By 2060, the power system will be dominated by new energy sources, with the proportion of installed non-fossil energy capacity at over 90% and the proportion of non-fossil energy generation at over 85%. Compared to that under the 2 °C temperature control target, the power sector under the 1.5 °C temperature control target needs to accelerate the pace of the low-carbon transition of electricity and deal with key issues such as the orderly withdrawal of coal power, the construction of a diversified clean energy system and the application of carbon capture devices. This study recommends that the process of building a zero-emissions power sector requires a good pace of the construction of new power systems at a suitable pace, increased efforts to tackle key technologies and improved relevant market mechanisms. China's carbon-neutral pathway in the power sector also has implications for other countries' clean, low-carbon transitions of their power systems.
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Luo, S.; Hu, W.; Liu, W.; Zhang, Z.; Bai, C.; Huang, Q.; Chen, Z. Study on the decarbonization in China’s power sector under the background of carbon neutrality by 2060. Renewable and Sustainable Energy Reviews 2022, 166, 112618 DOI: 10.1016/j.rser.2022.112618
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Study on the decarbonization in China's power sector under the background of carbon neutrality by 2060
Luo, Shihua; Hu, Weihao; Liu, Wen; Zhang, Zhenyuan; Bai, Chunguang; Huang, Qi; Chen, Zhe
Renewable & Sustainable Energy Reviews (2022), 166 (), 112618CODEN: RSERFH; ISSN:1364-0321. (Elsevier Ltd.)
The power sector in China, which is the main CO2 emission contributor in the country, plays an essential role in achieving the 2060 carbon neutrality goal. Notably, there are scientific gaps regarding the decarbonization plan to achieve this goal and the future power supply structure. The objective of this study is to systematically explore and evaluate the feasibility of constructing a carbon-neutral power sector. Considering the power source potential, power supply characteristics, and advanced technologies, methodol. steps were developed for the design and assessment of China's power sector. In particular, an evaluation indicator system was included to assess the decarbonization of the power sector and make it comparable in the international context. The results indicated that it is possible for the country's power sector to achieve carbon neutrality by 2060, using available domestic energy resources. The total cost of the 100% non-fossil power sector was the lowest, accounting for 87.3% of that of the business-as-usual (BAU) power sector. Compared with the BAU power sector, the renewable power sectors had abatement costs of -0.12-0.43 kCNY/t. The neg. abatement cost indicated that power sector decarbonization could be cost-effective in China. In the international context, the cost of electricity of the future China power sector (∼0.42 CNY/kWh) was comparable to that in other regions, while the CO2 abatement cost was lower than that in most regions. The proposed methodol. steps can be beneficial for CO2 emissions redn. and energy structure conversion in the power sector of any region.
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National Academies of Sciences, E.; Medicine Valuing climate damages: updating estimation of the social cost of carbon dioxide; National Academies Press: 2017; DOI: 10.17226/24651 .
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Cite this: Environ. Sci. Technol. 2023, 57, 11, 4396–4405

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Abstract
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Figure 1
Figure 1. MESSAGEix-CAEP model framework.
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Figure 2
Figure 2. CO₂ emissions under the SCN scenario from 2020 to 2060. a, Proportion of each sector’s carbon reduction between the BL and SCN scenarios. b, CO₂ emissions and peak time for each sector under the HCN scenario. The carbon emissions and energy consumption in 2020 are calculated by historical data. IS: iron and steel industry; TF: road traffic sector; PW: power industry; BL: building sector; CM: cement industry.
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Figure 3
Figure 3. Energy carbon reduction and consumption under the SCN scenario from 2020 to 2060. a, Proportion of energy carbon reduction between the BL and SCN scenarios. b, Proportion of primary energy consumption from 2020 to 2060 under the SCN scenario. The carbon emissions and energy consumption in 2020 are calculated by historical data. All energies are harmonized by converting them to standard coal (29307 kJ/kg). The consumption of renewable energies is calculated based on their power generation and converted through the theoretical power conversion coefficient (0.1229 kgce/kWh).
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Figure 4
Figure 4. Power consumption and industry contributions under the SCN scenario. The power consumption in 2020 is calculated by historical data.
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Figure 5
Figure 5. Marginal abatement cost curve. a, Marginal abatement cost curve for industries under the SCN scenario. b, Marginal abatement cost under the SCN and HCN scenarios.
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Figure 6
Figure 6. Annual carbon abatement costs and benefits from 2021 to 2060.
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References
This article references 45 other publications.
1. 1
  IPCC WGI AR6. Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., Zhou, B., Eds.; Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA, 2021; pp 3– 32. https://www.ipcc.ch/report/ar6/wg1/Chapter/summary-for-policymakers/ (accessed 2023-02-25).
  There is no corresponding record for this reference.
2. 2
  Shan, Y.; Guan, D.; Zheng, H.; Ou, J.; Li, Y.; Meng, J.; Mi, Z.; Liu, Z.; Zhang, Q. China CO2 emission accounts 1997–2015. Scientific Data 2018, 5 (1), 170201, DOI: 10.1038/sdata.2017.201
  2
  China CO2 emission accounts 1997-2015
  Shan, Yuli; Guan, Dabo; Zheng, Heran; Ou, Jiamin; Li, Yuan; Meng, Jing; Mi, Zhifu; Liu, Zhu; Zhang, Qiang
  Scientific Data (2018), 5 (1), 170201CODEN: SDCABS; ISSN:2052-4463. (Nature Research)
  China is the world's top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China's CO2 emissions is the first step in implementing redn. policies. However, no annual, officially published emissions data exist for China. The current emissions estd. by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions ests. is approx. equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement prodn.). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions redn. policy-making in China.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXovFSlug%253D%253D&md5=803fff0b4541593de7ee44d11bdd6665
3. 3
  Zheng, X.; Lu, Y.; Yuan, J.; Baninla, Y.; Zhang, S.; Stenseth, N. C.; Hessen, D. O.; Tian, H.; Obersteiner, M.; Chen, D. Drivers of change in China’s energy-related CO2 emissions. Proc. Natl. Acad. Sci. U. S. A. 2020, 117 (1), 29– 36, DOI: 10.1073/pnas.1908513117
  3
  Drivers of change in China's energy-related CO2 emissions
  Zheng, Xiaoqi; Lu, Yonglong; Yuan, Jingjing; Baninla, Yvette; Zhang, Sheng; Stenseth, Nils Chr.; Hessen, Dag O.; Tian, Hanqin; Obersteiner, Michael; Chen, Deliang
  Proceedings of the National Academy of Sciences of the United States of America (2020), 117 (1), 29-36CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  A review. CO2 emissions are of global concern because of climate change. China has become the largest CO2 emitter in the world and presently accounts for 30% of global emissions. Here, we analyze the major drivers of energy-related CO2 emissions in China from 1978 when the reform and opening-up policy was launched. We find that (1) there was a 6-fold increase in energy-related CO2 emissions, which was driven primarily (176%) by economic growth followed by population growth (16%), while the effects of energy intensity (-79%) and carbon intensity (-13%) slowed the growth of carbon emissions over most of this period; (2) energy-related CO2 emissions are pos. related to per capita gross domestic product (GDP), population growth rate, carbon intensity, and energy intensity; and (3) a portfolio of command-and-control policies affecting the drivers has altered the total emission trend. However, given the major role of China in global climate change mitigation, significant future redns. in China's CO2 emissions will require transformation toward low-carbon energy systems.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmtFKrsg%253D%253D&md5=17ad78adbe055473ef14c78ea2fead20
4. 4
  Guan, D.; Meng, J.; Reiner, D. M.; Zhang, N.; Shan, Y.; Mi, Z.; Shao, S.; Liu, Z.; Zhang, Q.; Davis, S. J. Structural decline in China’s CO2 emissions through transitions in industry and energy systems. Nature Geoscience 2018, 11 (8), 551– 555, DOI: 10.1038/s41561-018-0161-1
  4
  Structural decline in China's CO2 emissions through transitions in industry and energy systems
  Guan, Dabo; Meng, Jing; Reiner, David M.; Zhang, Ning; Shan, Yuli; Mi, Zhifu; Shao, Shuai; Liu, Zhu; Zhang, Qiang; Davis, Steven J.
  Nature Geoscience (2018), 11 (8), 551-555CODEN: NGAEBU; ISSN:1752-0894. (Nature Research)
  As part of the Paris Agreement, China pledged to peak its CO2 emissions by 2030. In retrospect, the commitment may have been fulfilled as it was being made-China's emissions peaked in 2013 at a level of 9.53 gigatons of CO2, and have declined in each year from 2014 to 2016. However, the prospect of maintaining the continuance of these redns. depends on the relative contributions of different changes in China. Here, we quant. evaluate the drivers of the peak and decline of China's CO2 emissions between 2007 and 2016 using the latest available energy, economic and industry data. We find that slowing economic growth in China has made it easier to reduce emissions. Nevertheless, the decline is largely assocd. with changes in industrial structure and a decline in the share of coal used for energy. Decreasing energy intensity (energy per unit gross domestic product) and emissions intensity (emissions per unit energy) also contributed to the decline. Based on an econometric (cumulative sum) test, we confirm that there is a clear structural break in China's emission pattern around 2015. We conclude that the decline of Chinese emissions is structural and is likely to be sustained if the nascent industrial and energy system transitions continue.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXht1OjsrvL&md5=bbf19d393bb088487d63050b3a4ca8b5
5. 5
  Rosa, E. A.; Dietz, T. Human drivers of national greenhouse-gas emissions. Nature Climate Change 2012, 2 (8), 581– 586, DOI: 10.1038/nclimate1506
  5
  Human drivers of national greenhouse-gas emissions
  Rosa, Eugene A.; Dietz, Thomas T.
  Nature Climate Change (2012), 2 (8), 581-586CODEN: NCCACZ; ISSN:1758-6798. (Nature Publishing Group)
  A review. Centuries of speculation about the causes of human stress on the environment is now being disciplined with empirical evidence, including analyses of differences in greenhouse-gas emissions across contemporary nation states. The cumulative results can provide useful guidance for both climate projections and for policy design. Growing human population and affluence clearly contribute to enhanced environmental stress. Evidence does not support the argument for amelioration of greenhouse-gas emissions at the highest levels of affluence. However, the role of other factors, such as urbanization, trade, culture and institutions remains ambiguous.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtV2isLvF&md5=4ae2cf24946f90f9cda1b2ab28010689
6. 6
  Cai, B.; Cao, L.; Lei, Y.; Wang, C.; Zhang, L.; Zhu, J.; Li, M.; Du, M.; Lv, C.; Jiang, H.; Ning, M.; Wang, J. China’s carbon emission pathway under the carbon neutrality target. China Population,Resources and Environment 2021, 31 (01), 7– 14
  There is no corresponding record for this reference.
7. 7
  Li, W.; Zhang, S.; Lu, C. Research on the driving factors and carbon emission reduction pathways of China’s iron and steel industry under the vision of carbon neutrality. Journal of Cleaner Production 2022, 361, 132237 DOI: 10.1016/j.jclepro.2022.132237
  7
  Research on the driving factors and carbon emission reduction pathways of China's iron and steel industry under the vision of carbon neutrality
  Li, Wei; Zhang, Shuohua; Lu, Can
  Journal of Cleaner Production (2022), 361 (), 132237CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
  Under the vision of carbon neutrality, China's iron and steel industry (CISI) urgently needs to achieve low-carbon development. To formulate effective and targeted emission redn. policies for CISI, the driving forces of carbon dioxide (CO2) emissions and future emission redn. pathways in CISI are explored in this paper. The Logarithmic Mean Divisia Index (LMDI) method and the Mean Impact Value (MIV) technique are adopted to analyze the driving factors of CO2 emissions in CISI at historical and prospective dimensions, resp. Furthermore, the extreme learning machine (ELM) model optimized by the bat algorithm (BA) is established to project the carbon emission redn. pathways of CISI during 2020-2050 under the business-as-usual (BAU) scenario, the low-speed, medium-speed, and high-speed development scenarios considering the constraint of the carbon neutrality target. The results reveal that prodn. capacity and energy efficiency are essential drivers of CO2 emissions in CISI. Consequently, aimed at achieving carbon neutrality, CISI should focus on eliminating backward capacity and simultaneously accelerating the deployment of advanced technologies. Addnl., it is difficult to accomplish the carbon neutrality goal by 2060 under the BAU scenario. Conversely, under the optimal emission redn. pathway detd. by the high-speed development scenario, CISI will reach its peak in 2022 with a peak value of 2143.42 million tons of CO2 (MtCO2). The av. annual emission abatement rate during 2022-2050 is maintained at approx. 4.47% and the cumulative redn. rate in 2050 will exceed 70% compared to the base year 2019. CISI is required to develop more stringent emission redn. measures to achieve significant emission abatement. The crude steel prodn. capacity should be reduced to 533 Mt in 2050 and the capacity utilization rate should be maintained beyond 80%. The energy consumption per ton of steel must be decreased to 264 Kg of coal equiv. (Kgce) in 2050.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhvFWhsb3F&md5=9d06e0cbfe0adf45f056e7dfc9f10b5b
8. 8
  Wang, H.; Lu, X.; Deng, Y.; Sun, Y.; Nielsen, C. P.; Liu, Y.; Zhu, G.; Bu, M.; Bi, J.; McElroy, M. B. China’s CO2 peak before 2030 implied from characteristics and growth of cities. Nature Sustainability 2019, 2 (8), 748– 754, DOI: 10.1038/s41893-019-0339-6
  There is no corresponding record for this reference.
9. 9
  Zhang, S.; Chen, W. Assessing the energy transition in China towards carbon neutrality with a probabilistic framework. Nature. Communications 2022, 13 (1), 87, DOI: 10.1038/s41467-021-27671-0
  9
  Assessing the energy transition in China towards carbon neutrality with a probabilistic framework
  Zhang, Shu; Chen, Wenying
  Nature Communications (2022), 13 (1), 87CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)
  A profound transformation of China's energy system is required to achieve carbon neutrality. Here, we couple Monte Carlo anal. with a bottom-up energy-environment-economy model to generate 3,000 cases with different carbon peak times, technol. evolution pathways and cumulative carbon budgets. The results show that if emissions peak in 2025, the carbon neutrality goal calls for a 45-62% electrification rate, 47-78% renewable energy in primary energy supply, 5.2-7.9 TW of solar and wind power, 1.5-2.7 PWh of energy storage usage and 64-1,649 MtCO2 of neg. emissions, and synergistically reducing approx. 80% of local air pollutants compared to the present level in 2050. The emission peak time and cumulative carbon budget have significant impacts on the decarbonization pathways, technol. choices, and transition costs. Early peaking reduces welfare losses and prevents overreliance on carbon removal technologies. Technol. breakthroughs, prodn. and consumption pattern changes, and policy enhancement are urgently required to achieve carbon neutrality.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xns1Ghug%253D%253D&md5=00160dcb7aaf66f80cb7f5a6dbfaf135
10. 10
  Langevin, J.; Harris, C. B.; Reyna, J. L. Assessing the Potential to Reduce U.S. Building CO2 Emissions 80% by 2050. Joule 2019, 3 (10), 2403– 2424, DOI: 10.1016/j.joule.2019.07.013
  There is no corresponding record for this reference.
11. 11
  Zhou, S.; Tong, Q.; Pan, X.; Cao, M.; Wang, H.; Gao, J.; Ou, X. Research on low-carbon energy transformation of China necessary to achieve the Paris agreement goals: A global perspective. Energy Economics 2021, 95, 105137 DOI: 10.1016/j.eneco.2021.105137
  There is no corresponding record for this reference.
12. 12
  van Sluisveld, M. A. E.; de Boer, H. S.; Daioglou, V.; Hof, A. F.; van Vuuren, D. P. A race to zero - Assessing the position of heavy industry in a global net-zero CO2 emissions context. Energy and Climate Change 2021, 2, 100051 DOI: 10.1016/j.egycc.2021.100051
  12
  A race to zero - Assessing the position of heavy industry in a global net-zero CO2 emissions context
  van Sluisveld, Mariesse A. E.; de Boer, Harmen Sytze; Daioglou, Vassilis; Hof, Andries F.; van Vuuren, Detlef P.
  Energy and Climate Change (2021), 2 (), 100051CODEN: ECCNA7; ISSN:2666-2787. (Elsevier B.V.)
  In this study, we explore the decarbonisation pathways of four carbon and energy-intensive industries (resp. iron & steel, clinker & cement, chems. and pulp & paper) in the context of a global 2050 net-zero carbon emissions objective using the IMAGE integrated assessment model. We systematically test the robustness of the model by studying its responses to four different decarbonisation narratives and across six different world regions. The study underpins earlier conclusions in the literature on 'residual emissions' and 'hard-to-abate sectors', such as the persistence of residual emissions and the overall continued use of fossil fuels by heavy industries within the global 2050 net-zero context (with the pulp & paper sector as an exception). However, under the condition that net-neg. emissions are achieved in the power and energy conversion sectors prior to the 2050 landmark, the indirect emission removals can compensate for the residual emissions left in the industry sectors, rendering these sectors 'net-zero' as early as the 2040s. Full decarbonisation of industrial (sub)sector(s) is found to be possible, but only under very specific narratives and likely outside of the 2050 timeline for the iron & steel, clinker & cement and the chem. sector. Subsequently, we find that the decarbonisation patterns in IMAGE are industry and regionally specific, though, different strategic considerations (narratives) did not substantially change the models' decarbonisation response before or after 2050. Important aspects of the decarbonisation responses are the (direct and indirect) electrification of the iron & steel sector, a full dependency on carbon removal technologies in the clinker & cement sector, the closing of carbon and material loops in the chem. sector and zero-carbon heating for the pulp & paper sector. However, further research and modeling efforts are needed to study a broader palette of conceivable decarbonisation pathways and implications for industry within a global 2050 net-zero economy context.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhtlWltrvL&md5=a3539d9ff66528309125ae0527092934
13. 13
  Duan, H.; Zhou, S.; Jiang, K.; Bertram, C.; Harmsen, M.; Kriegler, E.; van Vuuren, D. P.; Wang, S.; Fujimori, S.; Tavoni, M.; Ming, X.; Keramidas, K.; Iyer, G.; Edmonds, J. Assessing China’s efforts to pursue the 1.5°C warming limit. Science 2021, 372 (6540), 378– 385, DOI: 10.1126/science.aba8767
  13
  Assessing China's efforts to pursue the 1.5°C warming limit
  Duan, Hongbo; Zhou, Sheng; Jiang, Kejun; Bertram, Christoph; Harmsen, Mathijs; Kriegler, Elmar; van Vuuren, Detlef P.; Wang, Shouyang; Fujimori, Shinichiro; Tavoni, Massimo; Ming, Xi; Keramidas, Kimon; Iyer, Gokul; Edmonds, James
  Science (Washington, DC, United States) (2021), 372 (6540), 378-385CODEN: SCIEAS; ISSN:1095-9203. (American Association for the Advancement of Science)
  Given the increasing interest in keeping global warming below 1.5°C, a key question is what this would mean for China's emission pathway, energy restructuring, and decarbonization. By conducting a multimodel study, we find that the 1.5°C-consistent goal would require China to reduce its carbon emissions and energy consumption by more than 90 and 39%, resp., compared with the "no policy" case. Neg. emission technologies play an important role in achieving near-zero emissions, with captured carbon accounting on av. for 20% of the total redns. in 2050. Our multimodel comparisons reveal large differences in necessary emission redns. across sectors, whereas what is consistent is that the power sector is required to achieve full decarbonization by 2050. The cross-model avs. indicate that China's accumulated policy costs may amt. to 2.8 to 5.7% of its gross domestic product by 2050, given the 1.5°C warming limit.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXpvFajt78%253D&md5=f5cccf5df88a29de8884489a2b3b0b2b
14. 14
  Cao, J.; Dai, H.; Li, S.; Guo, C.; Ho, M.; Cai, W.; He, J.; Huang, H.; Li, J.; Liu, Y.; Qian, H.; Wang, C.; Wu, L.; Zhang, X. The general equilibrium impacts of carbon tax policy in China: A multi-model comparison. Energy Economics 2021, 99, 105284 DOI: 10.1016/j.eneco.2021.105284
  There is no corresponding record for this reference.
15. 15
  Zhao, X.-g.; Jiang, G.-w.; Nie, D.; Chen, H. How to improve the market efficiency of carbon trading: A perspective of China. Renewable and Sustainable Energy Reviews 2016, 59, 1229– 1245, DOI: 10.1016/j.rser.2016.01.052
  There is no corresponding record for this reference.
16. 16
  McPhearson, T.; M. Raymond, C.; Gulsrud, N.; Albert, C.; Coles, N.; Fagerholm, N.; Nagatsu, M.; Olafsson, A. S.; Soininen, N.; Vierikko, K. Radical changes are needed for transformations to a good Anthropocene. npj Urban Sustainability 2021, 1 (1), 5, DOI: 10.1038/s42949-021-00017-x
  There is no corresponding record for this reference.
17. 17
  IEA. Going carbon negative: What are the technology options? ; 2020. https://www.iea.org/commentaries/going-carbon-negative-what-are-the-technology-options (accessed 2023-02-25).
  There is no corresponding record for this reference.
18. 18
  IPCC WGI AR6. Summary for Policymakers. In Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Shukla, P. R., Skea, J., Slade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., Belkacemi, M.; Hasija, A., Lisboa, G., Luz, S., Malley, J., Eds.; Cambridge University Press: Cambridge, UK and New York, NY, USA, 2022. https://www.ipcc.ch/report/ar6/wg3/ (accessed 2023-02-25).
  There is no corresponding record for this reference.
19. 19
  IEA. Net Zero by 2050; IEA, Paris, License: CC BY 4.0; 2021. https://www.iea.org/reports/net-zero-by-2050 (accessed 2023-02-25).
  There is no corresponding record for this reference.
20. 20
  Huppmann, D.; Gidden, M.; Fricko, O.; Kolp, P.; Orthofer, C.; Pimmer, M.; Kushin, N.; Vinca, A.; Mastrucci, A.; Riahi, K.; Krey, V. The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. Environmental Modelling & Software 2019, 112, 143– 156, DOI: 10.1016/j.envsoft.2018.11.012
  There is no corresponding record for this reference.
21. 21
  Zhang, S.; Yi, B.; Guo, F.; Zhu, P. Exploring selected pathways to low and zero CO2 emissions in China’s iron and steel industry and their impacts on resources and energy. Journal of Cleaner Production 2022, 340, 130813 DOI: 10.1016/j.jclepro.2022.130813
  21
  Exploring selected pathways to low and zero CO2 emissions in China's iron and steel industry and their impacts on resources and energy
  Zhang, Shaohui; Yi, Bowen; Guo, Fei; Zhu, Pengyu
  Journal of Cleaner Production (2022), 340 (), 130813CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
  The increasing energy and material consumption assocd. with global economic growth has resulted in the need for more severe efforts at mitigating global climate change. The iron and steel industry consumes 8% of energy and emits 7% of total CO2 globally. China's iron and steel industry contributes to 15% of that country's total CO2 emissions. Therefore, there is an urgent need to explore the possibility of net zero emissions in the iron and steel industry in China to meet China's goal of carbon neutrality before 2060. In the study presented in this paper, the MESSAGEix-China iron and steel model was developed by integrating the process-based technol. of the sector into the IIASA's MESSAGEix framework to explore zero CO2 emission pathways and their assocd. impacts on resources, energy, and water in China's iron and steel industry up to 2100. We found that there are multiple pathways to achieving zero CO2 emissions in the Chinese iron and steel industry by the end of the 21st century. More specifically, in all the pathways developed in this study, CO2emissions decreased significantly between 2030 and 2060 due to the rapid application of 100% scrap-based Elec. Arc Furnaces (EAFs) and hydrogen-based Direct Reduced Iron (DRI)-EAFs steel-making technologies. However, by 2060, there will still be 70-360 Mt of CO2 emissions from China's iron and steel industry; consequently, carbon sink or neg. emission technologies are required to offset this and achieve the country's carbon neutrality goal. Furthermore, technologies for achieving zero emissions differ widely in terms of their impacts on the consumption of materials and energy. Compared to the elec. (ELE) scenarios, 25-40% of extra iron ore is consumed in the current and new national policy (NPS) scenarios and the DRI scenarios, but 25-220% of scrap is required. At the same time, 20-150% more energy will be saved in the ELE scenarios than in the NPS and DRI scenarios. Finally, we recommend that policy makers design a cross-cutting strategy to achieve zero CO2 emissions and enhance efforts for material recycling and the provision of clean energy and water.
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  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XmsFWmsb4%253D&md5=1d245f5a6f387eea0720198a8b8c2c4d
22. 22
  Dargay, J.; Gately, D.; Sommer, M. Vehicle ownership and income growth, worldwide: 1960–2030. energy journal 2007, 28 (4), 143, DOI: 10.5547/ISSN0195-6574-EJ-Vol28-No4-7
  There is no corresponding record for this reference.
23. 23
  Medlock, K. B.; Soligo, R. Car ownership and economic development with forecasts to the year 2015. Journal of Transport Economics and Policy 2002, 36 (2), 163– 188
  There is no corresponding record for this reference.
24. 24
  Wu, T.; Zhao, H.; Ou, X. Vehicle Ownership Analysis Based on GDP per Capita in China: 1963–2050. Sustainability 2014, 6 (8), 4877– 4899, DOI: 10.3390/su6084877
  There is no corresponding record for this reference.
25. 25
  Dargay, J.; Gately, D. Income’s effect on car and vehicle ownership, worldwide: 1960–2015. Transportation Research Part A: Policy and Practice 1999, 33 (2), 101– 138, DOI: 10.1016/S0965-8564(98)00026-3
  There is no corresponding record for this reference.
26. 26
  Huo, H.; Wang, M. Modeling future vehicle sales and stock in China. Energy Policy 2012, 43, 17– 29, DOI: 10.1016/j.enpol.2011.09.063
  There is no corresponding record for this reference.
27. 27
  Tan, M.; Li, X.; Xie, H.; Lu, C. Urban land expansion and arable land loss in China─a case study of Beijing–Tianjin–Hebei region. Land Use Policy 2005, 22 (3), 187– 196, DOI: 10.1016/j.landusepol.2004.03.003
  There is no corresponding record for this reference.
28. 28
  Gong, T.; Zhang, W.; Liang, J.; Lin, C.; Mao, K. Forecast and Analysis of the Total Amount of Civil Buildings in China in the Future Based on Population Driven. Sustainability 2021, 13 (24), 14051, DOI: 10.3390/su132414051
  There is no corresponding record for this reference.
29. 29
  Huo, T.; Xu, L.; Feng, W.; Cai, W.; Liu, B. Dynamic scenario simulations of carbon emission peak in China’s city-scale urban residential building sector through 2050. Energy Policy 2021, 159, 112612 DOI: 10.1016/j.enpol.2021.112612
  29
  Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050
  Huo, Tengfei; Xu, Linbo; Feng, Wei; Cai, Weiguang; Liu, Bingsheng
  Energy Policy (2021), 159 (), 112612CODEN: ENPYAC; ISSN:0301-4215. (Elsevier Ltd.)
  Understanding future trajectory of urban residential building carbon emissions (URBCE) is essential to seeking effective carbon-abatement pathways to combat climate change. However, future evolutionary trajectory, possible emission peaks and peaking times in this sector are still unclear. This study innovatively develops an integrated dynamic simulation model by embedding a bottom-up building end-use energy model into the system dynamics model. Based on this, scenario anal. approach is combined with Monte Carlo simulation method to explore the possible emission peaks and peaking times considering the uncertainties of impact factors. We apply the integrated SD-LEAP model to Chongqing, a typical city in Chinas hot-summer and cold-winter zone. Results show that URBCE will probably peak at 22.8 (±8.0) Mt CO2 in 2042 (±3.4)-well beyond Chinas 2030 target. Different building end-uses present substantial disparities. The contribution of combined heating and cooling to URBCE mitigation will be over 60% between business-as-usual and low-carbon scenarios. Dynamic sensitivity anal. reveals that per capita gross domestic product, carbon emission factor, and residential floor space per capita can boost emission peaks and peaking time. This study can not only boost the theory and model development for carbon emission prediction, but also assist governments to set effective carbon-redn. targets and policies.
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30. 30
  O’Neill, B. C.; Carter, T. R.; Ebi, K.; Harrison, P. A.; Kemp-Benedict, E.; Kok, K.; Kriegler, E.; Preston, B. L.; Riahi, K.; Sillmann, J.; van Ruijven, B. J.; van Vuuren, D.; Carlisle, D.; Conde, C.; Fuglestvedt, J.; Green, C.; Hasegawa, T.; Leininger, J.; Monteith, S.; Pichs-Madruga, R. Achievements and needs for the climate change scenario framework. Nature Climate Change 2020, 10 (12), 1074– 1084, DOI: 10.1038/s41558-020-00952-0
  30
  Achievements and needs for the climate change scenario framework
  O'Neill Brian C; Carlisle David; Green Carole; O'Neill Brian C; Carter Timothy R; Ebi Kristie; Harrison Paula A; Kemp-Benedict Eric; Kok Kasper; Kriegler Elmar; Preston Benjamin L; Riahi Keywan; van Ruijven Bas J; Sillmann Jana; Fuglestvedt Jan; van Vuuren Detlef; van Vuuren Detlef; Conde Cecilia; Hasegawa Tomoko; Leininger Julia; Monteith Seth; Pichs-Madruga Ramon
  Nature climate change (2020), 10 (12), 1074-1084 ISSN:1758-678X.
  Long-term global scenarios have underpinned research and assessment of global environmental change for four decades. Over the past ten years, the climate change research community has developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. Here we assess how well this framework is working and what challenges it faces. We synthesize insights from scenario-based literature, community discussions and recent experience in assessments, concluding that the framework has been widely adopted across research communities and is largely meeting immediate needs. However, some mixed successes and a changing policy and research landscape present key challenges, and we recommend several new directions for the development and use of this framework.
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31. 31
  O’Neill, B. C.; Kriegler, E.; Ebi, K. L.; Kemp-Benedict, E.; Riahi, K.; Rothman, D. S.; van Ruijven, B. J.; van Vuuren, D. P.; Birkmann, J.; Kok, K. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global environmental change 2017, 42, 169– 180, DOI: 10.1016/j.gloenvcha.2015.01.004
  There is no corresponding record for this reference.
32. 32
  Chen, Y.; Guo, F.; Wang, J.; Cai, W.; Wang, C.; Wang, K. Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. Scientific Data 2020, 7 (1), 1– 13, DOI: 10.1038/s41597-020-0421-y
  There is no corresponding record for this reference.
33. 33
  McKitrick, R. A Derivation of the Marginal Abatement Cost Curve. Journal of Environmental Economics and Management 1999, 37 (3), 306– 314, DOI: 10.1006/jeem.1999.1065
  There is no corresponding record for this reference.
34. 34
  Kesicki, F.; Strachan, N. Marginal abatement cost (MAC) curves: confronting theory and practice. Environmental Science & Policy 2011, 14 (8), 1195– 1204, DOI: 10.1016/j.envsci.2011.08.004
  There is no corresponding record for this reference.
35. 35
  DECC, D. f. E. a. C. C. Carbon Valuation in UK Policy Appraisal: A Revised Approach ; 2009.
  There is no corresponding record for this reference.
36. 36
  Kesicki, F. Marginal abatement cost curves for policy making–expert-based vs. model-derived curves ; 2010. https://www.homepages.ucl.ac.uk/~ucft347/Kesicki_MACC.pdf (accessed 2023-02-25).
  There is no corresponding record for this reference.
37. 37
  Du, L.; Hanley, A.; Wei, C. Estimating the Marginal Abatement Cost Curve of CO2 Emissions in China: Provincial Panel Data Analysis. Energy Economics 2015, 48, 217– 229, DOI: 10.1016/j.eneco.2015.01.007
  There is no corresponding record for this reference.
38. 38
  Ma, C.; Hailu, A.; You, C. A critical review of distance function based economic research on China’s marginal abatement cost of carbon dioxide emissions. Energy Economics 2019, 84, 104533 DOI: 10.1016/j.eneco.2019.104533
  There is no corresponding record for this reference.
39. 39
  Xiao, H.; Wei, Q.; Wang, H. Marginal abatement cost and carbon reduction potential outlook of key energy efficiency technologies in China′s building sector to 2030. Energy Policy 2014, 69, 92– 105, DOI: 10.1016/j.enpol.2014.02.021
  There is no corresponding record for this reference.
40. 40
  Cai, W.; He, N.; Li, M.; Xu, L.; Wang, L.; Zhu, J.; Zeng, N.; Yan, P.; Si, G.; Zhang, X.; Cen, X.; Yu, G.; Sun, O. J. Carbon sequestration of Chinese forests from 2010 to 2060: spatiotemporal dynamics and its regulatory strategies. Science Bulletin 2022, 67 (8), 836– 843, DOI: 10.1016/j.scib.2021.12.012
  40
  Carbon sequestration of Chinese forests from 2010 to 2060: spatiotemporal dynamics and its regulatory strategies
  Cai, Weixiang; He, Nianpeng; Li, Mingxu; Xu, Li; Wang, Longzhu; Zhu, Jianhua; Zeng, Nan; Yan, Pu; Si, Guoxin; Zhang, Xiaoquan; Cen, Xiaoyu; Yu, Guirui; Sun, Osbert Jianxin
  Science Bulletin (2022), 67 (8), 836-843CODEN: SBCUA5; ISSN:2095-9281. (Elsevier B.V.)
  Forestation is important for sequestering atm. carbon, and it is a cost-effective and nature-based soln. (NBS) for mitigating global climate change. Here, under the assumption of forestation in the potential plantable lands, we used the forest carbon sequestration (FCS) model and field survey involving 3365 forest plots to assess the carbon sequestration rate (CSR) of Chinese existing and new forestation forests from 2010 to 2060 under three forestation and three climate scenarios. Without considering the influence of extreme events and human disturbance, the estd. av. CSR in Chinese forests was 0.358 ± 0.016 Pg C a-1, with partitioning to biomass (0.211 ± 0.016 Pg C a-1) and soil (0.147 ± 0.005 Pg C a-1), resp. The existing forests account for approx. 93.5% of the CSR, which will peak near 2035, and decreasing trend was present overall after 2035. After 2035, effective tending management is required to maintain the high CSR level, such as selective cutting, thinning, and approx. disturbance. However, new forestation from 2015 in the potential plantable lands would play a minimal role in addnl. CSR increases. In China, the CSR is generally higher in the Northeast, Southwest, and Central-South, and lower in the Northwest. Considering the potential losses through deforestation and logging, it is realistically estd. that CSR in Chinese forests would remain in the range of 0.161-0.358 Pg C a-1 from 2010 to 2060. Overall, forests have the potential to offset 14.1% of the national anthropogenic carbon emissions in China over the period of 2010-2060, significantly contributing to the carbon neutrality target of 2060 with the implementation of effective management strategies for existing forests and expansion of forestation.
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41. 41
  Huang, Y.; Sun, W.; Qin, Z.; Zhang, W.; Yu, Y.; Li, T.; Zhang, Q.; Wang, G.; Yu, L.; Wang, Y.; Ding, F.; Zhang, P. The role of China’s terrestrial carbon sequestration 2010–2060 in offsetting energy-related CO2 emissions. National Science Review 2022, 9 (8), nwac057 DOI: 10.1093/nsr/nwac057
  There is no corresponding record for this reference.
42. 42
  Zhuo, Z.; Du, E.; Zhang, N.; Nielsen, C. P.; Lu, X.; Xiao, J.; Wu, J.; Kang, C. Cost increase in the electricity supply to achieve carbon neutrality in China. Nature. Communications 2022, 13 (1), 3172, DOI: 10.1038/s41467-022-30747-0
  42
  Cost increase in the electricity supply to achieve carbon neutrality in China
  Zhuo, Zhenyu; Du, Ershun; Zhang, Ning; Nielsen, Chris P.; Lu, Xi; Xiao, Jinyu; Wu, Jiawei; Kang, Chongqing
  Nature Communications (2022), 13 (1), 3172CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)
  Abstr.: The Chinese government has set long-term carbon neutrality and renewable energy (RE) development goals for the power sector. Despite a precipitous decline in the costs of RE technologies, the external costs of renewable intermittency and the massive investments in new RE capacities would increase electricity costs. Here, we develop a power system expansion model to comprehensively evaluate changes in the electricity supply costs over a 30-yr transition to carbon neutrality. RE supply curves, operating security constraints, and the characteristics of various generation units are modelled in detail to assess the cost variations accurately. According to our results, approx. 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in the power system by 2050. The electricity supply costs would increase by 9.6 CNY¢/kWh. The major cost shift would result from the substantial investments in RE capacities, flexible generation resources, and network expansion.
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43. 43
  Wu, G.; Niu, D. A study of carbon peaking and carbon neutral pathways in China’s power sector under a 1.5 °C temperature control target. Environmental Science and Pollution Research 2022, 29 (56), 85062– 85080, DOI: 10.1007/s11356-022-21594-z
  43
  A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5 °C temperature control target
  Wu Gengqi; Niu Dongxiao; Wu Gengqi; Niu Dongxiao
  Environmental science and pollution research international (2022), 29 (56), 85062-85080 ISSN:.
  The clean and low-carbon transition of China's power sector is of great importance to the achievement of dual carbon targets and the control of global warming. This paper first estimates the remaining carbon budget of the power sector under a 1.5 °C temperature control target and on this basis constructs 1.5 °C and 2 °C power transition scenarios, examining key boundary conditions such as economic development and changes in the cost of power generation technologies. Second, the Genetic Algorithm-Extreme Learning Machine (GA-ELM) model is used to forecast the electricity demand for the next forty years. Finally, with the objective of minimising the total planning cost, a pathway optimisation model of the power system is constructed to explore the optimal transition path for the power system using the dual carbon target, carbon budget and electricity demand as the main constraints. The results of the study show that the carbon budget of the Chinese power sector is approximately 7.1 × 10(10) t CO2 for a 1.5 °C temperature control target. The electricity demand tends to saturate after 2050 and reaches 1.58 × 10(13) kWh in 2060. The time of the carbon peak and carbon neutralisation in the power sector is 5 years ahead of the double carbon target. By 2060, the power system will be dominated by new energy sources, with the proportion of installed non-fossil energy capacity at over 90% and the proportion of non-fossil energy generation at over 85%. Compared to that under the 2 °C temperature control target, the power sector under the 1.5 °C temperature control target needs to accelerate the pace of the low-carbon transition of electricity and deal with key issues such as the orderly withdrawal of coal power, the construction of a diversified clean energy system and the application of carbon capture devices. This study recommends that the process of building a zero-emissions power sector requires a good pace of the construction of new power systems at a suitable pace, increased efforts to tackle key technologies and improved relevant market mechanisms. China's carbon-neutral pathway in the power sector also has implications for other countries' clean, low-carbon transitions of their power systems.
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44. 44
  Luo, S.; Hu, W.; Liu, W.; Zhang, Z.; Bai, C.; Huang, Q.; Chen, Z. Study on the decarbonization in China’s power sector under the background of carbon neutrality by 2060. Renewable and Sustainable Energy Reviews 2022, 166, 112618 DOI: 10.1016/j.rser.2022.112618
  44
  Study on the decarbonization in China's power sector under the background of carbon neutrality by 2060
  Luo, Shihua; Hu, Weihao; Liu, Wen; Zhang, Zhenyuan; Bai, Chunguang; Huang, Qi; Chen, Zhe
  Renewable & Sustainable Energy Reviews (2022), 166 (), 112618CODEN: RSERFH; ISSN:1364-0321. (Elsevier Ltd.)
  The power sector in China, which is the main CO2 emission contributor in the country, plays an essential role in achieving the 2060 carbon neutrality goal. Notably, there are scientific gaps regarding the decarbonization plan to achieve this goal and the future power supply structure. The objective of this study is to systematically explore and evaluate the feasibility of constructing a carbon-neutral power sector. Considering the power source potential, power supply characteristics, and advanced technologies, methodol. steps were developed for the design and assessment of China's power sector. In particular, an evaluation indicator system was included to assess the decarbonization of the power sector and make it comparable in the international context. The results indicated that it is possible for the country's power sector to achieve carbon neutrality by 2060, using available domestic energy resources. The total cost of the 100% non-fossil power sector was the lowest, accounting for 87.3% of that of the business-as-usual (BAU) power sector. Compared with the BAU power sector, the renewable power sectors had abatement costs of -0.12-0.43 kCNY/t. The neg. abatement cost indicated that power sector decarbonization could be cost-effective in China. In the international context, the cost of electricity of the future China power sector (∼0.42 CNY/kWh) was comparable to that in other regions, while the CO2 abatement cost was lower than that in most regions. The proposed methodol. steps can be beneficial for CO2 emissions redn. and energy structure conversion in the power sector of any region.
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45. 45
  National Academies of Sciences, E.; Medicine Valuing climate damages: updating estimation of the social cost of carbon dioxide; National Academies Press: 2017; DOI: 10.17226/24651 .
  There is no corresponding record for this reference.
Supporting Information
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.2c08171.
- Details description of the CNTD platform, the database for each sector, supplemental methods for marginal abatement cost, sensitive analysis for different vehicle ownership saturation levels, and additional results regarding the decarbonization pathways, energy consumption, interaction between sectors, and marginal abatement cost under HCN scenario (PDF)
- es2c08171_si_001.pdf (1.44 MB)
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Closing the Gap between Carbon Neutrality Targets and Action: Technology Solutions for China’s Key Energy-Intensive SectorsClick to copy article linkArticle link copied!

Environmental Science & Technology

Abstract

Synopsis

1. Introduction

2. Methods and Data

2.1. China Carbon Neutrality Technology Database

2.2. Modeling Framework

Figure 1

2.3. Projection of Future “Product” Demand for the Road Traffic and Building Sector

2.3.1. Projection of Chinese Future Vehicle Ownership

2.3.2. Projection of Chinese Future Housing Demand

2.4. Scenario Design

2.5. Marginal Abatement Cost

3. Results

3.1. CO2 Emissions

Figure 2

3.2. Energy Consumption

Figure 3

3.3. Industry Technology Adoptions and Interactions

Figure 4

3.4. Marginal Abatement Cost

Figure 5

3.5. Cost-Benefit Analysis

Figure 6

4. Policy Implication

5. Discussion

Supporting Information

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Author Information

Acknowledgments

References

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Abstract

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Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

References

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Supporting Information

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Closing the Gap between Carbon Neutrality Targets and Action: Technology Solutions for China’s Key Energy-Intensive Sectors
Click to copy article linkArticle link copied!

3.1. CO₂ Emissions