Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

Bibliometric review of carbon peak with CiteSpace: evolution, trends, and framework

In the context of global climate change, countries around the world are actively implementing carbon peak and carbon neutrality goals. In-depth research on carbon peak can help improve environmental conditions and achieve a harmonious coexistence between economic development and environmental protection. However, a comprehensive review of the current status of research in this area is scarce. Therefore, this article explores the current research evolution and hotspots of "carbon peak" with the help of CiteSpace visualization software, predicts the future development trends, and builds a knowledge network framework. A comprehensive analysis of the research on carbon peak from multiple perspectives is presented. The results show that the number of papers published on carbon peak is increasing every year, and that carbon peak has become a widely participated research area. Publications from various institutions and journals have also attracted widespread attention. The research hotspots of carbon peak have constantly changed with time, resulting in many theoretical and technological innovations. The knowledge framework of the field is constructed on this basis, which gives readers a clearer understanding of the development and trends in the field and provides some reference and help for future researchers.

Driving technology factors of carbon emissions: Theoretical framework and its policy implications for China

Empirical studies have widely examined the driving factors and methods to achieve a carbon peak; however, they seldom construct a theoretical framework and ignore the potential heterogeneity in technology. The most notable controversy is technology's different roles in carbon emissions. This study proposes an integrated theoretical framework considering the evolution of carbon emissions and presents the conditions for achieving a carbon peak. This framework shows that if the positive role of eco-friendly technology in decreasing carbon emissions is larger than the negative role of production-oriented technology in increasing carbon emissions; thus, carbon emissions do not increase (i.e. carbon peak). Additionally, this framework addresses the controversy concerning the effect of technology on carbon emissions. Our empirical results from a city-level panel dataset show that China is still moving towards achieving carbon emission reduction. Analysis of the driving mechanism reveals that production-oriented technology increases carbon emissions by increasing the production scale, consequently demanding more energy and emitting more carbon dioxide.

Prediction and balanced allocation of thermal power carbon emissions from a provincial perspective of China

Carbon control in the thermal power generation industry is crucial for achieving the overall carbon peak target. How to predict, evaluate, and balance the allocation of inter provincial carbon emissions has a significant impact on the decision-making of reasonable allocation of inter provincial carbon emissions in the target year. Therefore, this paper uses Monte Carlo-ARIMA-BP neural network and ZSG-DEA model to conduct temporal trend prediction and carbon emission quota allocation research. We propose the "intra provincial and inter provincial" framework for carbon emissions trading in thermal power plants, which aims to break through the barriers in carbon emission rights exchange among provinces. The conclusions are as follows: (1) the growth trend of carbon emissions from thermal power is gradually slowing down and is expected to peak before 2030. (2) Inner Mongolia, Jiangsu, and Shandong have high input-output efficiency, and are all the main output provinces for carbon emission quota allocation. After being adjusted using the ZSG-DEA model, they can still be at the forefront of efficiency. (3) The "intra provincial and inter provincial" framework for carbon emissions trading can effectively predict and allocate the carbon emission demand of each province from time and space dimensions, balance the carbon emission rights and interests of each province, and provide forward-looking planning suggestions for inter provincial carbon emission rights exchange.

Regional CO2 accounting and market layout of incinerator fly ash management in China

The reduction of greenhouse gas (GHG) emissions from solid waste incinerator fly ash (IFA) management attracts growing interests since China's zero-waste plan and carbon peak/neutral goals. Herein, provincial GHG emissions from four demonstrated IFA reutilization technologies in China were estimated after analyzing IFA spatial-temporal distribution. Results indicate that technologies transition (landfilling-to-reutilization) could reduce GHG except for glassy slag production. IFA to cement option could potentially realize negative GHG emissions. Spatial GHG variation drivers in IFA management were recognized as provincial-different IFA composition and power emission factors. IFA management options were recommended provincially after weighting local development goals related to GHG reduction and economic benefits. Baseline scenario analysis shows that China's IFA industry would reach carbon peak in 2025 (5.02 Mt). 2030's GHG reduction potential (6.12 Mt) is equivalent to that of absorbed CO₂ by 340 million trees annually. Overall, this research could contribute to illustrating future market layout complying with carbon peaking.

Analysis of China's non-ferrous metals industry's path to peak carbon: A whole life cycle industry chain based on copper

Copper is an essential nonferrous metal, and the adjustment of its whole industry chain structure is conducive to realizing a carbon peak in the nonferrous metal industry. We have performed a life cycle assessment to calculate the carbon emissions of the copper industry. Based on the carbon emissions scenarios of shared socioeconomic pathways (SSPs), we have utilized material flow analysis and system dynamics to analyze the structural changes in the copper industry chain from 2022 to 2060 in China. The results show that (1) the flows and in-use stocks of all types of copper resources will increase significantly. The overall copper supply may meet demand around 2040-2045 due to secondary copper production potentially replacing primary copper production to a large extent, and trade supply is the primary pathway for meeting copper demand. (2) The total carbon emissions from the regeneration system are the smallest (4 %), followed by the production and trade subsystems, accounting for 48 %. The embodied carbon emissions from copper product trade in China have expanded annually. (3) Under the SSP scenario, the copper chain carbon emission peak will be achieved by approximately 2040. Based on a balanced copper supply and demand scenario, the recycled copper recovery efficiency must reach 84.6 %, and the energy structure (the proportion of non-fossil energy in electricity) must reach 63.8 % by 2030 to achieve the carbon peak target for the copper industry chain in China. The above conclusions indicate that actively promoting adjustments in the energy structure and resource recovery processes may help encourage the carbon peak of nonferrous metals in China by realizing the carbon peak of the copper industry.

Can China achieve its 2030 and 2060 CO2 commitments? Scenario analysis based on the integration of LEAP model with LMDI decomposition

China's ambitious targets of peaking its Carbon dioxide (CO₂) emissions on or before 2030 and achieving carbon neutrality by 2060 have been a topic of discussion in the international community. This study innovatively combines the logarithmic mean Divisia index (LMDI) decomposition method and the long-range energy alternatives planning (LEAP) model to quantitatively evaluate the CO₂ emissions from energy consumption in China from 2000 to 2060. Using the Shared Socioeconomic Pathways (SSPs) framework, the study designs five scenarios to explore the impact of different development pathways on energy consumption and related carbon emissions. The LEAP model scenarios are based on the result of LMDI decomposition, which identifies the key influencing factors on CO₂ emissions. The empirical findings of this study demonstrate that the energy intensity effect is the primary factor of the 14.7 % reduction in CO₂ emissions observed in China from 2000 to 2020. Conversely, the economic development level effect has been the driving factor behind the increase of 50.4 % in CO₂ emissions. Additionally, the urbanization effect has contributed 24.7 % to the overall change in CO₂ emissions during the same period. Furthermore, the study investigates potential future trajectories of CO₂ emissions in China up to 2060, based on various scenarios. The results suggest that, under the SSP1 scenarios. China's CO₂ emissions would peak in 2023 and achieve carbon neutrality by 2060. However, under the SSP4 scenarios, emissions are expected to peak in 2028, and China would need to eliminate approximately 2000 Mt of additional CO₂ emissions to reach carbon neutrality. In other scenarios, China is projected to be unable to meet the carbon peak and carbon neutrality goals. The conclusions drawn from this study offer valuable insights for potential policy adjustments to ensure that China could fulfill its commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060.

The effect exerted by environment regulation on industrial structure optimization: Evidence of 286 China's cities on the prefecture level

Promoting industrial structure optimization and improvement on the basis of environment regulation based on "China's carbon peak and carbon neutralization" turns out to be a requirement that cannot be avoided for achieving China's economic growth with high quality. In this study, a dynamic game model for enterprises and governments in local areas with two phases, covering a polluting production sector and a clean production sector, is built for analyzing the influence mechanism of environment regulation of local governments on industrial structure optimization. Panel data of 286 cities on the prefecture level and above from 2003 to 2018 served as samples. The direct and dynamic impacts of environment regulation on industrial structure optimization are empirically tested, and the threshold model is adopted to test whether industrial structures and resource endowment will affect the effect of environment regulation on industrial structure optimization. Lastly, the effect exerted by environment regulation on industrial structure optimization is tested by region. The empirical results show that there is a nonlinear correlation of environment regulation and industrial structure optimization. Once the environment regulation intensity reaches a certain inflection point, it will hinder industrial structure optimization. When regional resource endowment and the secondary industry's ratio are used as threshold variables, environment regulation has a threshold effect on industrial structure optimization. The effect exerted by environment regulation on industrial structure optimization has regional heterogeneity.

Research on the articulated coupling effect of carbon tax policy under resource endowment in China

The carbon tax is a policy tool that internalizes external costs through a tax mechanism, which helps to reduce the consumption of fossil energy and lower carbon dioxide emissions. China, as the largest carbon emitter, introducing a carbon tax can further enhance the effectiveness of emission reduction. However, the introduction of a carbon tax may exacerbate contradictions in other aspects of the social system. To this end, the paper establishes a dynamic model of the carbon tax system by combining grey system theory and the IPAT model and then explores the coupling effect of the carbon tax on the economy, energy, and environment under the premise of China's resource endowment. It is found that carbon tax will not only distort consumer behavior but also aggravate the degree of capital market distortion. In the time-series simulation, it is found that the emission reduction efficiency of the carbon tax will show an oscillation decline. The carbon tax undermines the carbon peak target by dampening demand for energy consumption. In addition, we also find that the change of energy structure is the root of driving the failure of the "Jevons Paradox" and the realization of the "environmental Kuznets curve," and the panel data of energy and economy are only the manifestation of these two phenomena. China needs to adjust its energy structure to achieve its carbon peaking target. These results are helpful for policymakers to rationally view the carbon peaking target and formulate reasonable emission reduction policies.

The carbon footprint response to projected base stations of China's 5G mobile network

While the rapid expansion of China's 5G mobile network helps to speed up the nation's economic and social development, it tends to release more CO₂ due to the 5G's significant energy demand, hampering sustainable development of the 5G network. Previous assessments of CO₂ emissions from China's 5G development were based on a projected 5G network ranging from six to fifteen million base stations with the absent of a convincing business model in 5G's application. Under the scenario of business-estimated six million base stations in 2030, the share of electricity consumed by China's 5G networks in 2030 could reach 8.4 % of the national total power generation, causing 0.44 GtCO₂/yr CO₂ emissions. We collected 5G base station numbers in 2020 and 2021 in 31 provinces and province-level municipalities (PLM), the period with the rapid growth of the 5G base stations in China. We linked these provincial base stations with provincial Gross Domestic Product (GDP), population (POP), and big data development level (BDDL) and established a statistical model to predict 5G base stations by 2030. The model predicted 2-5 million 5G base stations by 2030, considerably lower than the business-projected base station number. Under the model predicted 5G base stations, China's 5G network could yield 0.15-0.29 GtCO₂/yr emissions subject to the nation's BDDL from 40 to 80 % by 2030. Both 5G base stations and CO₂ emissions are significantly lower than the previous estimates. We decomposed the CO₂ footprint of China's 5G networks and assessed the contribution of the number of 5G base stations and mobile data traffic to 5G-induced CO₂ emissions. We find that increasing the application of clean energy and promoting energy efficiency can reduce CO₂ emissions in the 5G network. To more accurately estimate 5G's climate effect, we propose that it urgently needs to improve vivid 5G business models.

Interprovincial differences in the historical peak situation of building carbon emissions in China: Causes and enlightenments

Scientific assessment of the historical carbon peak situation of provincial buildings in China is the premise and basis for understanding the country's development trends and formulating carbon peak goals. The population size, urbanization stages, economic development levels, natural resources endowment, and energy structure characteristics vary significantly for the different provinces in China, resulting in significant differences in the peaking situation of building carbon emissions (BCE). The differences require more attention given the current environmental status. Based on the judgment function of carbon peaking conditions and the statistical Mann-Kendall (MK) trend test method, this study evaluates the historical peak situation of building carbon emissions at the provincial level in China. The peaking sequence of BCE, building carbon emissions per capita (BCEP), and carbon emissions per unit floor area (BCEA) were analyzed, and the driving factors that cause different carbon peak situations were discussed. Further, with reference to the experience of the United States, a peak strategy for building carbon emissions in China was proposed. The research results showed that BCE in Beijing and Yunnan have peaked, and the three provinces of Shanghai, Sichuan, and Hubei have plateaued. The most important factors that cause different peaking situations for BCE are the floor area per capita and carbon emissions per unit of energy consumption. In addition, the peak order of building carbon emissions was BCEA, BCEP, and BCE. A strategy that should be adopted in the promotion of buildings' carbon peak in China is to formulate phased peak goals for BCE, BCEP, and BCEA at a national level and differentiated echelon peak goals at a provincial level considering interprovincial differences. This study provides a scientific basis and decision-making reference for formulating a path to buildings' carbon peak at a provincial level in China.

Analysis of influencing factors and prediction of carbon emissions of typical urban agglomerations in China: a case study of Beijing-Tianjin-Hebei region

Achieving the "double carbon" goal is a major task and challenge facing China. The emission reduction actions in typical urban agglomerations are of great significance. Based on the consideration of the impact of regional coordinated development, this study analyzed influencing factors and conducted prediction of carbon emissions from terminal energy consumption in the Beijing-Tianjin-Hebei (BTH) region. Firstly, the factors affecting carbon emissions were screened through the STIRPAT model. Then, the paper designs different scenarios and finally uses the genetic algorithm extreme learning machine (GA-ELM) algorithm to predict the carbon emissions of the BTH region, with and without considering the impact of the coordinated development strategy. The research shows that the increase in energy intensity and the improvement of energy consumption structure have the largest promotion effect on carbon emission reduction. At the same time, the significant role of the coordinated development strategy in promoting regional carbon emission reduction was verified. Therefore, the BTH region should adhere to the path of coordinated development, innovate low-carbon technology, and deepen the concept of green consumption to promote the realization of regional carbon emission reduction goals.

Greenhouse gas emissions and peak trend of commercial vehicles in China

Commercial vehicles are important within the context of global warming, since they exhibit greenhouse gas (GHG) emissions that are disproportionate to their quantity. The aim of this study was to create a bottom-up GHG emissions assessment model which considers GHG emissions of newly produced commercial vehicles and those in current use. Through this study, the number of future commercial vehicles were predicted, thereby facilitating a simulation of future GHG emissions. Our results show that the total GHG emissions of commercial vehicles in 2019 was 580 million t CO_2-eq.. Among them, the GHG emissions stemming from the production of new commercial vehicles accounted for ∼0.3% of the emissions, whereas the use stage accounted for more than 99.0%. Moreover, the future ownership of commercial vehicles depends on GDP and the demand of freight and passenger transport. The ownership of commercial vehicles was predicted about 36.61 million in 2025, 45.44 million in 2030 and 55.85 million in 2035. The carbon peak of commercial vehicles varies across different scenarios, peaking around 2031-2034, at 680-780 million t CO_2-eq.. This study systematically simulated the carbon peak of commercial vehicles, contributing toward a deeper understanding of commercial vehicles within the context of GHG emissions. These results can be applied toward creating quantitatively-driven pathways for carbon peak or neutrality targets in the commercial vehicle sector.

Fiscal decentralization, industrial structure upgrading, and carbon emissions: evidence from China

The debate over the role of fiscal decentralization and industrial structure upgrading in China's environmental governance has received increasing attention. Based on China's provincial panel data from 2003 to 2019, this paper investigates the impact of fiscal decentralization and industrial structure upgrading on carbon emissions to provide empirical evidence for the above theoretical argument. The results show that fiscal decentralization and industrial structure upgrading are negatively correlated with carbon emissions, while the interaction term for fiscal decentralization with industrial structure upgrading presents a facilitating effect on carbon emissions. Besides, fiscal decentralization, industrial structure upgrading, and the interaction term have significant regional heterogeneity on carbon emissions. When fiscal decentralization and industrial structure upgrading are taken as threshold variables, the effects of industrial structure upgrading and fiscal decentralization are significantly nonlinear. Moreover, environmental regulation, transportation infrastructure, and carbon emissions are positively correlated. There exists an inverted U-shaped relationship between carbon emissions and economic growth, which proves environmental Kuznets curve theorem. However, FDI and urbanization have no significant effect on carbon emissions. According to the above conclusions, it is necessary to strengthen the positive interaction between fiscal decentralization and industrial structure upgrading in mitigating carbon emissions, promoting the green and low-carbon transformation of China's economy, thus realizing the goals of "carbon peak" and "carbon neutrality."

Identifying carbon emission characteristics and carbon peak in China based on the perspective of regional clusters

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region.

A data-driven approach for the measurement and improvement of regional industrial ecological efficiency for carbon peaking and carbon neutralization

Industrial ecological efficiency is regarded as an urgent challenge that affects the development of ecological civilization and environmental governance. Here, we propose a data-driven approach to measure and promote regional industrial ecological efficiency. We collected data related to regional industrial development and used the Data Envelopment Analysis-Banker Charnes and Cooper (DEA-BCC) model to measure regional industrial ecological efficiency from a static perspective. The Malmquist index model was then used to measure regional industrial ecological efficiency from a dynamic perspective. In addition, we used a Tobit regression model to identify the factors affecting regional industrial ecological efficiency. Through a case study of regional industrial ecological efficiency, we demonstrate the specific application of the proposed data-driven approach. This study provides a new and effective tool for improving industrial ecological efficiency at a regional scale. This method can help enterprises and local governments improve industrial ecological efficiency, coordinate the relationship between industrial economic growth and the ecological environment, and boost regional efforts to achieve carbon peaking and carbon neutralization goals.

Forecasting China's natural gas external dependence under the "Dual Carbon" goals by a new grey model

As a low-carbon and cost-effective clean energy source, natural gas plays an important role in achieving China's "Dual Carbon" target. In this article, a new three-parameter discrete grey prediction model is used to simulate and forecast the production and consumption of natural gas in China from the perspective of background value optimization. Then the minimum mean absolute percentage error as the objective function from the perspective of fractional order cumulative generation in the real number field. Last, a fractional order in the real number field three parameter discrete grey prediction model TDGM(1,1,z,r(R)) is constructed under the condition of optimal background value. Then we use the model to simulate and predict China's Natural Gas External Dependence (NGED) under the "Dual Carbon" target. The results show that the performance of the new model is better than that of the traditional model GM(1,1) and DGM(1,1), thus proving the practicability and effectiveness of the new model. Put forward relevant policy suggestions according to the prediction results of China's NGED, and provide decision-making reference for the Chinese government to achieve the "Dual Carbon" goals.

Historical trend and drivers of China's CO2 emissions from 2000 to 2020

China is the largest CO2 emitter in the world and announced that carbon peak and neutral targets will be achieved before 2030 and 2060, respectively. A retrospective analysis of past CO2 emissions and their drivers is important for the actions of peaking CO2 emissions before 2030 in China. CO2 emissions from energy use (coal, oil, and natural gas) and cement production from 2000 to 2020 were calculated first, and their drivers were decomposed into economic and population growth, energy intensity, and emission coefficient by logarithmic mean Divisa index (LMDI) analysis in this study. China's CO2 emissions increased nearly threefold from 3385 in 2000 to 10,788 million tonnes (Mt) in 2020, with a decline from 2013 to 2016. Coal was the major emission sector contributing more than 70% in most years, while natural gas emissions increased nearly 13 times from 53 to 723 Mt in the two decades, although its contribution only accounted for 6.7% in 2020. Economic growth was the major positive driver, while energy intensity reduction was the major negative driver of the emission increments by year and by the Five Year Plan (FYP). Emission coefficient reduction gradually became important due to its negative effect, especially in the 13th FYP, which offset ~ 30% of the emissions induced by economic growth. The projections of CO2 emissions in 2025, 2030, and 2035 could be 11,596 ± 582, 11,774 ± 621, and 11,401 ± 672 Mt, respectively, suggesting that China's carbon emissions could peak around 2030 with an increment of ~ 1000 Mt on the 2020 levels. Under the sustainable growth of the economy and population, it is possible to reduce the carbon peak value or achieve peak time earlier through the additional reduction of energy intensity and emission coefficient by technological progress and energy alternatives such as non-fossil fuels.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02811-8.

How does digital economy affect carbon emissions? Evidence from global 60 countries

The digital economy is of great significant for countries to achieve carbon neutrality and carbon peak. Using country-level panel data from 2008 to 2018, this study empirically examined the impact of the development of the digital economy on carbon emissions and the associated transmission mechanisms by using the intermediary effect model. Our main findings are as follows. (1) The level of digital economy development varies greatly between countries, and the difference between "hyper-digitalized countries" and "under-connected countries" is increasingly obvious. (2) Development of the digital economy significantly reduces the carbon emission intensity, but promotes increases in the per capita carbon emissions. (3) Analysis shows that economic growth, financial development, and industrial structure upgrading play mediating roles between the digital economy and carbon emissions. Our study not only advances the study on digital economy and carbon emissions, but also provides a significant reference for policy makers to achieve carbon peak and carbon neutrality.

A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5 °C temperature control target

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 × 10¹⁰ t CO2 for a 1.5 °C temperature control target. The electricity demand tends to saturate after 2050 and reaches 1.58 × 10¹³ 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.

Environmental and Resource Impacts from an Aggressive Regionalized Carbon Peak Policy

When and how to achieve a carbon peak is a concern for provincial regions within China under the context of achieving carbon neutrality in 2060. This study investigates the overall carbon peak environmental and resource impacts under current national targets and Shanghai's latest more aggressive carbon peak policy by using a dynamic multiple-region computable general equilibrium (CGE) model for the year 2030. Results show that (1) the national carbon peak and the more aggressive regional policy in Shanghai will result in energy consumption and carbon emissions decreases when compared to the business-as-usual scenario in most provinces; (2) although some cobenefits in water use reduction may occur in most provinces under the carbon policy scenarios, the results show positive and negative variations; (3) provincial level environmental and resources in transport, electricity, metal smelting and pressing, and agricultural production sectors are most influenced by Shanghai's aggressive carbon peak policy; and (4) the outsourced environmental and resource impacts from Shanghai to other provinces are very significant under Shanghai's aggressive carbon policy. These relevant results provide insights to facilitate broader governance decision-making for environmental resource nexuses while seeking an improved understanding of global sustainable development and climate governance.

Achievements, challenges and global implications of China's carbon neutral pledge

China has been committed to achieving carbon neutrality by 2060. China's pledge of carbon neutrality will play an essential role in galvanising global climate action, which has been largely deferred by the Covid-19 pandemic. China's carbon neutrality could reduce global warming by approximately 0.2-0.3 °C and save around 1.8 million people from premature death due to air pollution. Along with domestic benefits, China's pledge of carbon neutrality is a "game-changer" for global climate action and can inspire other large carbon emitters to contribute actively to mitigate carbon emissions, particularly countries along the Belt and Road Initiative (BRI) routes. In order to achieve carbon neutrality by 2060, it is necessary to decarbonise all sectors in China, including energy, industry, transportation, construction, and agriculture. However, this transition will be very challenging, because major technological breakthroughs and large-scale investments are required. Strong policies and implementation plans are essential, including sustainable demand, decarbonizing electricity, electrification, fuel switching, and negative emissions. In particular, if China can peak carbon emissions earlier, it can lower the costs of the carbon neutral transition and make it easier to do so over a longer time horizon. China's pledge of carbon neutrality by 2060 and recent pledges at the 26^th UN Climate Change Conference of the Parties (COP26) are significant contributions and critical steps for global climate action. However, countries worldwide need to achieve carbon neutrality to keep the global temperature from growing beyond the level that will cause catastrophic damages globally.

Could China's long-term low-carbon energy transformation achieve the double dividend effect for the economy and environment?

Exploring the low-carbon energy transformation pathway is vital to coordinate economic growth and environmental improvement for achieving China's carbon peak target. Three energy-target scenarios are developed in this paper, considering the targets of energy structure, electrification rate, and carbon mitigation towards 2030 announced by the Chinese government. A dynamic multi-sectoral computable general equilibrium model, CHINAGEM, is employed to examine the economic and environmental effects under different pathways of long-term low-carbon transformation. It detects that China's energy structure would substantially transfer to the low-carbon and clean one, whereas CO₂, SO₂, and NO_X emissions in 2020-2030 would vastly abate along with all three energy-target scenarios. Different pathways would produce varying positive impacts on China's macro-economy and achieve the different extent of double dividend effects. It is highly conceivable for China to peak its carbon emission at 12.4 GtCO₂ by 2028 if it serves the comparatively more stringent low-carbon transformation pathways.

The empirical decomposition and peak path of China's tourism carbon emissions

Carbon emissions from tourism are an important indicator to measure the impact of tourism on environmental quality. As the world's largest industry, tourism has many related industries and is a strong driver of energy consumption. The emission reductions it can achieve will directly determine whether China's overall carbon emission reduction target can be met. This paper analyzes the drivers of the evolution of carbon emissions from the tourism industry in China over the period 2000-2017 as a research sample using the Generalized Dividing Index Method (GDIM), and on this basis, it uses scenario analysis and Monte Carlo simulation to predict the carbon peak in tourism for the first time. The research results show that the scale of industry and energy consumption are the key factors leading to increased tourism carbon emissions, and the carbon intensity of tourism industry, energy consumption carbon intensity, investment efficiency, and energy intensity are the main factors leading to reduced carbon emissions from tourism. The scale of investment and the carbon intensity of investment have a dual effect; the scenario analysis and Monte Carlo simulation used to predict peak carbon in China's tourism industry show that the peak carbon will occur approximately in 2030. The government needs to further guide and encourage the tourism industry to increase investment activities targeting energy conservation and emission reduction. Under the conditions of strictly implementing energy conservation and emission reduction measures and vigorous promotion of the transformation and upgrading of tourism development methods, the tourism industry will have considerable potential to reduce carbon emissions.

Impacts of the carbon emission trading system on China's carbon emission peak: a new data-driven approach

Over the past four decades, China's extensive economic growth mode has led to substantial greenhouse gas emissions, and China has become the world's largest emitter since 2009. In order to alleviate the dual pressures from international climate negotiations and domestic environmental degradation, the Chinese government has pronounced it will reach its emission peak before 2030. However, through analyzing 12 scenarios, we found that it will be very difficult to meet this ambitious goal under the current widely used policies. With the trial implementation of China's carbon emission trading system (ETS), concerns arise over whether national ETS can accelerate the carbon peak process. In this paper, we propose a new proactive data envelopment analysis approach to investigate the impacts of national carbon ETS on carbon peak. Several important results are obtained. For example, we find that carbon ETS has a significant accelerating effect on carbon peak, which effect will advance the carbon peak by one to 2 years, and the corresponding peak values are reduced by 2.71-3 Gt. In addition, the setting of carbon price in the current Chinese pilot carbon market is found to be overly conservative. Last, our estimation on the carbon trading volume indicates that the ETS lacks vitality as the annual average carbon trading volume only represents approximately 4.3% of the total average carbon emissions. Based on these findings, several policy implications are suggested regarding the means by which China can more smoothly peak its carbon emissions before 2030 and implement national carbon ETS.