Carbon Emission Reduction Policies Research Articles

Reduced interest option pricing for green bonds

PurposeThis paper originally proposed the fuzzy option pricing method for green bonds. Based on the requirements of arbitrage equilibrium, this paper draws on Merton's corporate bond option pricing model.Design/methodology/approachDescribing the asset value behavior of green bond issuing enterprises through diffusion-jump processes to reflect the uncertainty brought by carbon emission reduction policies and technologies, using approximation methods to get the analytical pricing formula and then, using a fuzzification technique of Choquet expectation under λ-additive fuzzy measures after considering fuzzy factors, the paper provides fuzzy intervals for the parity coupon rates of green bonds with different subjective levels for investors.FindingsThe paper proposes and argues the classical and fuzzy option pricing methods in turn for both corporate ordinary bonds and green bonds, considering carbon risk or climate risk. It implements the scenario analysis varying with industry emission standards and discusses the sensitiveness of the related key parameters of the option.Practical implicationsThe fuzzy option pricing for the green bonds provides the scope of the variable equilibrium values, operational theoretical supports and some policy implications of carbon reduction and promoting green funding.Originality/valueThe logic of introducing the fuzziness of the option pricing for the green bonds lies with considering the existence of fuzzy information about the project supported by the green bond and the subjectivity of investors and it also responds to changes in technological uncertainty and policy uncertainty in the process of “carbon peaking and carbon neutrality.”

Using Game Theory to Explore the Multinational Supply Chain Production Inventory Models of Various Carbon Emission Policy Combinations

This study uses Stackelberg game theory, considering different combinations of carbon emission reduction policies and that high-carbon-emission enterprises may face various carbon emission reduction regulations, to explore the production inventory problems in a multinational supply chain system. The purpose is to determine the manufacturer’s optimal production, shipping, carbon reduction investment, and the retailer’s replenishment under the equilibrium for different carbon emission policy combinations. To develop the production inventory models, this study first develops the total profit and carbon emission functions of the supply chain members, respectively, and then obtains the optimal solutions and total profits of the manufacturer and the retailer under different carbon emission policy combinations through the mathematical analysis method. Further, this study used several numerical examples to solve and compare the proposed models. The results of numerical analysis show that regardless of the increase in carbon price or carbon tax, the manufacturer and retailer will adjust their decisions to reduce carbon emissions. Specifically, an increase in the carbon price contributes to an increase in the total profit of manufacturers, while an increase in the carbon tax reduces the total profit of manufacturers. This study also explores a sensitivity analysis on the main parameters and has yielded meaningful management insights. For instance, in cases where low-carbonization strategies are required, the manufacturer or retailer can effectively reduce the carbon emissions resulting from production or purchasing activities, thereby significantly reducing overall carbon emissions. It is believed that the results of this study can provide enterprises/supply chains with reference to their respective production, transportation, carbon reduction investment, and inventory decisions under carbon emission policies, as well as information on partner selection and how to adjust decisions under environmental changes.

Sustainable-Driven Renovation of Existing Residential Buildings in China: A Systematic Exploration Based on Review and Solution Approaches

Under the backdrop of China’s national strategy to achieve carbon neutrality by 2060, efforts are underway across governmental, corporate, societal, and individual sectors to actively explore energy-saving renovations in existing buildings. Given that residential buildings constitute a significant proportion of the total energy consumption throughout the lifecycle of buildings in China, sustainable renovation of residential structures can contribute significantly to implementing China’s carbon emission reduction policies. While there exists a plethora of technological means in the market aimed at improving the energy performance of residential buildings, there still needs to be a more systematic discussion on the framework for sustainable renovation of existing Chinese residential buildings, with knowledge dissemination still needing to be more cohesive. In this context, this paper provides a comprehensive review of the field, utilizing bibliometric methods. Through a systematic review of selected peer-reviewed literature from the Web of Science and Scopus databases, the study focuses on the sustainable renovation of existing Chinese residential buildings, categorizing the process into three main stages: sustainable renovation, building performance simulation and suitability assessment. The paper also reviews the research methods adopted by previous researchers in the renovation, simulation and assessment stages, considering various optimization algorithms, variables, objectives, and software tools. Subsequently, the paper synthesizes a research framework comprising these three stages combined for different research objectives, aiming to assist policymakers, designers, and researchers in gaining a comprehensive understanding of the implementation status of sustainable renovation in existing Chinese residential buildings, identifying barriers to implementation, and formulating more efficient renovation policies and strategies for the future.

Spatiotemporal Dynamics of CO2 Emissions in China Based on Multivariate Spatial Statistics

With China’s rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China’s carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are “Low–Low” at the provincial level, and “Low–Low” and “High–High” at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China’s carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies.

Construction and Application of the Double Game Model for Direct Purchase of Electricity by Large Consumers under Consideration of Risk Factors

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers’ contract bidding and large consumers’ electricity purchase strategy in the medium- and long-term contract transactions.

Evaluation of marginal abatement cost and potential reduction in China's industrial carbon emissions: A quadratic directional output distance function approach

As a relevant indicator for quantifying the value of carbon emissions, the marginal abatement cost (MAC) of carbon emissions has received increasing attention from academics and policymakers. This study uses a quadratic directional output distance function to analyse the technical efficiency, potential emission reduction and shadow prices of China's industrial carbon emissions. The results show that the technical efficiency of China's industry gradually decreased during the sample period. From the perspective of regions with different carbon intensity and industrial output, regions with lower carbon intensity and higher industrial output gradually closed to the optimal frontier for the technology use level and had relatively high technical efficiency. Rising technical inefficiency and carbon emissions led to a potential emission reduction that also exhibits an upwards trend. In addition, the MAC of industrial carbon emissions exhibits a U-shaped trajectory, with 2011 as the turning point. Generally, in recent years, regions with lower carbon intensity and those with higher industrial output have a relatively high MAC for industrial carbon emissions. The empirical findings reveal that a carbon emission reduction policy should be formulated and targeted to each province, and the principle of ‘easy before difficult’ can be followed to promote emission reduction.

Inequality characteristics and influencing factors of CO2 emissions per capita in Jiangsu Province, China.

Analyzing the inequality characteristics and influencing factors of CO2 emissions per capita (CEPC) is conducive to balancing regional development and CO2 emissions reduction. This study applied the Gini coefficient and Theil index to investigate the CEPC inequalities during 2005-2017 at the county level in Jiangsu Province, China. Considering the spatial spillover and interaction effects, the factors influencing CEPC were analyzed by a hierarchical spatial autoregressive model. The results showed that the inequalities in CEPC first increased and then decreased at the inter-regional, and inter-county levels. The spatial pattern of CEPC was stable, and there was a significantly positive spatial autocorrelation of CEPC at the county level. The High-High type counties were mainly located in Sunan (southern Jiangsu). The spatial interaction effects of the CEPC between the prefecture and county levels indicated that governments at the prefecture level should integrate their county governments to reduce the CEPC. Moreover, carbon intensity, GDP per capita, land urbanization, and industrial structure play an important role in reducing CEPC. Our findings provide a scientific basis for formulating reasonable and effective carbon emission reduction policies.

Impact of three carbon emission reduction policies on carbon verification behavior: An analysis based on evolutionary game theory

Carbon tax and emission trading compound policy (CT&ET), carbon tax policy (CTP), and emission trading policy (ETP) support countries in achieving "dual carbon" goals. Carbon verification is essential to these policies' efficacy. Policy changes will affect system equilibrium and present unclear risks to policy implementation. This paper compared the impact of three carbon emission reduction policies on the behavior of carbon verification subjects under various conditions, three policies' benefits and drawbacks at different stages, and proposed a dynamic penalty mechanism to restrain subject infractions. Finally, the simulations revealed three policies of system evolution path and parameter sensitivity in various scenarios. CT&ET best guarantees carbon verification quality has a high-risk tolerance, and restricts infractions in most scenes. The excessively conservative carbon quota and severe collusion penalty make CTP optimal for limiting ECE false reports. ETP reduces collusion best when carbon quotas are appropriately allocated. Adjusting taxes and carbon prices, increasing verification levels, and reducing false reporting will be easier under CTP and ETP. Dynamic penalties are particularly effective in limiting infractions. Static penalties stabilize compliance behavior effectively. This study proposed a policy parameter framework and optimization mechanism to improve the carbon verification mechanism and accelerate the "dual carbon" goal.

Soil organic matter degradation and methylmercury dynamics in Hg-contaminated soils: Relationships and driving factors

Biodegradation of soil organic matter (SOM), which involves greenhouse gas (GHG) emissions, plays an essential role in the global carbon cycle. Over the past few decades, this has become an important research focus, particularly in natural ecosystems. SOM biodegradation significantly affects contaminants in the environment, such as mercury (Hg) methylation, producing highly toxic methylmercury (MeHg). However, the potential link between GHG production from SOM turnover in contaminated soils and biogeochemical processes involving contaminants remains unclear. In this study, we investigated the dynamics of GHG, MeHg production, and the relationship between biogeochemical processes in soils from two typical Hg mining sites. The two contaminated soils have different pathways, explaining the significant variations in GHG and MeHg production. The divergence of the microbial communities in these two biogeochemical processes is essential. In addition to the microbial role, abiotic factors such as Hg species can significantly affect MeHg production. On the other hand, we found an inverse relationship between CH4 and MeHg, suggesting that carbon emission reduction policies and management could inadvertently increase the MeHg levels. This highlights the need for an eclectic approach to organic carbon sequestration and contaminant containment. These findings suggest that it is difficult to establish a general pattern to describe and explain the SOM degradation and MeHg production in contaminated soils within the specific scenarios. However, this study provides a case study and helpful insights for further understanding the links between environmental risks and carbon turnover in Hg mining areas.

Are carbon emissions trading and green financial instruments synergistic? -Comprehensive quantitative research based on content analysis.

Coordinating policies is an essential guarantee for carbon emission reduction and sustainable development. Based on the theoretical framework of the policy paradigm, we quantitatively analyze 266 policy documents on promoting carbon emission trading and green financial policies from 2011 to 2022 using the content analysis research method. Based on the matching network of "policy objectives-policy tools," we analyze the synergistic characteristics of carbon emission trading policies and green financial policies in promoting carbon emission reduction targets and reveal the matching mode of "objectives-tools" of green financial policies by using social network analysis. It is found that, first, from the perspective of policy objectives, the main policy objectives of carbon emissions trading are to promote green innovation of enterprises, and the main policy objectives of green finance are to promote green development, which reflects the consistency and endogenous motivation of policy objectives. Secondly, command-control and market incentive policy tools are the main policy tools in the structure of policy tools. The proportion of public participation policy tools is small, and there is a structural asymmetry. Third, carbon emissions trading tools focus on supervision, adjustment, and platform construction. The green financial policy tools have the characteristics of guidance, public welfare, and externality. The two constitute a complementary, embedded, and integrated ' double synergy ' carbon emission reduction policy. Based on this, this paper puts forward some suggestions to promote policy coordination and provides a reference for China to achieve the dual carbon goal.

Spatial-temporal characteristics and influencing factors of carbon emission in Chengdu-Chongqing area: an urban transportation perspective.

The Chengdu-Chongqing twin-city economic circle is a vital growth pole and a new power source for Chinese high-quality development. Studying the spatial-temporal characteristics of carbon emissions and the role of factors affecting them under the transportation perspective is of great significance for this region to realize the carbon peak and carbon neutrality and to formulate carbon emission reduction policies. We use the exploring spatial data analysis (ESDA) and spatial regression model combined with the STIRPAT model, and research finding: (1) The total carbon emissions in the research area gradually increased from 2014 to 2020, but the growth rate showed a significant decline in 2019. (2) There is significant spatial heterogeneity of carbon emissions in the study area; the hotspot areas of total carbon emissions are in Chongqing and Chengdu, forming a high-low aggregation of carbon emissions. Per capita carbon emissions show a high trend in the southwest and a low in the northeast. (3) From the factors of transportation perspective, highway density and private vehicles have a positive impact on carbon emissions, and urban road areas and public transportation have a very significant inhibition of carbon emissions and a spatial spillover effect. (4) Other factors, such as population size, national economic development, urbanization level, and industrial structure, all have a positive effect on carbon emissions, and disposable income has a negative effect on carbon emissions.

Which model is more efficient in carbon emission prediction research? A comparative study of deep learning models, machine learning models, and econometric models.

Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econometric models and deep learning, but few works have systematically compared and analyzed the forecasting performance of the methods. Therefore, the paper makes a comparison for deep learning model, machine learning model, and the econometric model to demonstrate whether deep learning is an efficient method for carbon emission prediction research. In model mechanism, neural network for deep learning refers to an information processing model established by simulating biological neural system, and the model can be further extended through bionic characteristics. So the paper further optimizes the model from the perspective of bionics and proposes an innovative deep learning model based on the memory behavior mechanism of group creatures. Comparison results show that the prediction accuracy of the heuristic neural network is higher than that of the econometric model. Through in-depth analysis, the heuristic neural network is more suitable for predicting future carbon emissions, while the econometric model is more suitable for clarifying the impact of influencing factors on carbon emissions.

Impact of urban land development on carbon emission performance based on a multidimensional framework

Massive carbon emissions resulting from global urbanization have been one of the main triggers of climate warming. Because of its scale and representativeness, China's urbanization has significantly affected the world's socioeconomic system. Therefore, a comprehensive understanding of the urban land development (ULD) process and its impact on carbon emission performance (CEP), using cities in China as an example, is crucial for achieving global climate goals. Thus, using 245 cities in China as examples, a new explanatory framework was developed to understand the multidimensional characteristics of ULD and their impact on CEP. The average ULD increased from 0.266 to 0.299 and stabilized from 2000 to 2019, with the gap between cities continuously narrowing. Average CEP increased from 0.634 to 0.663 and then decreased to 0.556, presenting an increasing distribution pattern from northwest to southeast. The coupling level between ULD and CEP decreased from the east coast inland. More than half of the cities retained their coupling levels, and 3.67% achieved leapfrog development. The higher the initial CEP level, the weaker the positive effect of an increase in ULD level on the coupling score. In addition, the influences of the multidimensional characteristics of ULD on CEP showed significant spatiotemporal heterogeneity at the country scale. Thus, this study proposes that the effect of the ULD on CEP is phased, which is highly correlated with the evolution of the coupling level. These findings highlight the necessity for city managers to incorporate the differential impacts of the multidimensional characteristics of ULD on CEP in the formulation of carbon emission reduction policies.

A new multiregional carbon emissions forecasting model based on a multivariable information fusion mechanism and hybrid spatiotemporal graph convolution network

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (−7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly.

The impact of carbon tax policy on residents’ welfare of China and its heterogeneity under the carbon neutrality goal: A CGE model-based analysis

Under the background that China will achieve the goal of carbon neutrality by 2060, analyzing the impact of carbon neutral target constraints on the welfare of urban and rural residents is an important part of the design of a carbon emission reduction policy mechanism, in this paper, the dynamic recursive CGE(Computable general equilibrium) model is used to simulate the changes in welfare level, income consumption, and CO2 emissions of residents and their urban-rural heterogeneity under the policy of imposing a carbon tax on the fossil energy industry from 2022 to 2060. The results show that: compared with the BAU scenario, by 2060, first the income and consumption of residents will decrease slightly. Second, the target constraint of carbon neutrality makes the equivalent variation and compensate variation of urban and rural residents' welfare decrease slightly, and the equivalent variation and compensate variation of urban residents decrease more than those of rural residents. The following conclusions are drawn: first, the carbon-neutral target constraint makes the welfare of urban and rural residents decline slightly, but it will not have a strong impact on residents’ income and consumption. Second, this constraint has a greater impact on the income, consumption and savings of urban residents than those of rural residents.

탄소배출권 거래가 전남지역 산업 생산활동에 미치는 영향

When comparing South Korea's emission trading market on a global scale, such as in Europe, the United States, and China, South Korea possesses the hardware infrastructure required for the operation of an emission trading system. However, it appears to be lacking in the software aspect, including the implementation of various carbon reduction policies to complement the carbon emission trading system. The study empirically analyzes the impact of the carbon emission trading system on industries in Jeonnam. First, the carbon emission trading system does not seem to significantly constrain short-term production activities of businesses. Second, when examining the impact of carbon emission prices (KAU, EUA) on major industries in the Jeonnam region, carbon-related variables act as information intermediaries. It is evident that the chemical and steel industries in Jeonnam play the role of information recipients. Third, concerning the connectivity network with KAU as the central variable, it is apparent that the chemical and steel industries in Jeonnam receive more influence from KAU rather than influencing it. In the case of the connectivity network with EUA as the central variable, EUA plays the role of information recipients for the chemical and steel industries. This can be attributed to the participation of these industries in the European carbon emission trading market while exporting chemical and steel products to Europe, affecting EUA demand and prices. In the future, the impact of carbon emission reduction policies is likely to increase for domestic industries and companies in the Jeonnam region in the short term. To achieve regional and national carbon neutrality, policy measures tailored to the industrial structure characteristics of the Jeonnam region must be developed.

Evaluation of the effect of a low-carbon green city policy on carbon abatement in South Korea: A city-level analysis based on PSM-DID and LSA models

Climate change response is a long-term, stage-specific global challenge that necessitates ongoing research on its routes. Although previous research has examined policy planning and indicator development for a low-carbon transition based on a variety of macropolicies, more research is needed to determine whether such policies can help reduce carbon emissions and accelerate urban transitions. Based on 2015–2021 panel data covering 17 South Korean first-level administrative districts, we employ a propensity score matching-difference in difference (PSM-DID) model and a mediating effect model to address issues of interregional development faults and policy feasibility and to investigate the impacts of the low-carbon green city (LCGC) policy in different regions on urban carbon emissions. To address the development gap and policy feasibility across regions, this study investigates the mechanism and path of the effect of the LCGC policy on urban carbon dioxide emissions in various regions, ultimately incorporating local spatial autocorrelation (LSA) to investigate the characteristics of the differences at the spatial level. The findings demonstrate that (1) the present regulations effectively limit total urban carbon emissions but are characterized by strong rebound elasticity and a short period. (2) Energy use and renewable energy production have the greatest influence on reducing total urban carbon emissions. (3) The local spatial autocorrelation clustering characteristics present the clustering characteristics of being high in the east and low in the north. The HH agglomerations have the largest coverage and are mainly distributed in the southeast and southwest directions of the study area. The results of the study provide a decision-making basis and theoretical support for relevant decision-makers to formulate differentiated carbon emission reduction policies and to eliminate the current transition dilemma with a new research methodology.

Renewable energy consumption, carbon dioxide emission and financial stability: does institutional quality matter?

ABSTRACT In the context that countries around the world are making efforts to reduce carbon emissions and promote the use of renewable energy, the impact of these issues on the economy is becoming more and more concerned. Our study investigates the impact of renewable energy consumption and carbon dioxide emissions on financial stability and the role of institutional quality in mitigating these effects. Using panel data from 147 countries, including both developed and developing countries, and applying the quantile regression method, this study found that both renewable energy consumption and carbon dioxide emissions reduce financial stability. Moreover, the negative effects of carbon dioxide emissions on financial stability intensify in countries with higher financial stability. Additionally, institutional quality was found to play an important role in mitigating the negative impacts of renewable energy consumption and carbon dioxide emissions on financial stability, especially in countries with high levels of financial stability; the impact was insignificant in developing countries with low levels of financial stability. Our results can be useful for regulators in both developed and developing countries to control the negative impacts of carbon emission reduction and renewable energy policies on financial stability.

Land-use carbon emissions and built environment characteristics: A city-level quantitative analysis in emerging economies

Rapid urbanization and subsequent urban land expansions have led to land use changes, emerging as a prominent source of anthropogenic carbon emissions in the developing world. While numerous studies have quantified land-use carbon emissions (LUCE), there is still a lack of rigorous statistical analyses examining the driving factors of LUCE, particularly in the post-2015 era, when stringent environmental pollution regulations and carbon emission reduction policies have been implemented worldwide. This study aims to assess city-level LUCE in emerging economies and to further investigate the drivers of inter-city variations in LUCE through an extended STIRPAT model, taking Guangdong Province in China as an example. The results indicate a substantial increase in LUCE from 2000 to 2020, reflecting a surge in energy consumption associated with land-use transitions from natural land to construction land. The growing disparity between direct and indirect carbon emissions underscores the increasing significance of construction land planning and management in achieving emission reduction goals. Furthermore, our findings from the extended STIRPAT model shed light on carbon neutrality pathways by revealing the potential for LUCE reduction through adopting sustainable built environment planning.

Driving factors and peaking of CO2 emissions: An empirical analysis of Hunan Province

Understanding the driving factors of CO2 emissions and analyzing its trend is of great significance for formulating and optimizing carbon emission reduction policies. Taking Hunan Province as an example, this study adopted the LMDI method to investigate the driving factors of CO2 emissions in 2005–2020, and then presented the strategy for peaking emissions by 2030 via the simulation method. Results show that economic activity (output), number of vehicles, power generation of standard coal, and energy intensity of the production sector are the main driving factors affecting to CO2 emissions, with an average contribution of 7.54 %, 2.27 %, 1.95 %, and −6.86 %, respectively. Simulation results show that Hunan would likely achieve carbon peaking around 2030, within the peak range of 308.5–325.2 million tons CO2 depending on the economic growth rate setting. Moreover, the industrial and energy production and processing conversion sectors are key sectors for achieving a carbon peak, with average emissions contributions exceeding 35 % and 25 % during peak years, respectively. Overall, the study proposes a simple and reliable benchmark to investigate the pathway to achieve carbon peaking and provide a reference for other provinces and countries at the same development stage to achieve carbon peaking.