Carbon Constraints Research Articles

Sources and Fates of Sedimentary Polycyclic Aromatic Hydrocarbons in the East Siberian Arctic Shelf: Implications for Input Pathways and Black Carbon Constraint

AbstractThe Arctic region is experiencing more rapid climate changes than the other parts of the world and serves as a sink for semi‐volatile persistent organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), which can be utilized as molecular markers for pyrogenic carbon, such as black carbon (BC). As the sea ice retreats and increased terrestrial inputs with widespread wildfires, the PAH concentrations in the Arctic Ocean are rising. In this study, the sources and fates of PAHs together with BC in surface sediments from the East Siberian Arctic Shelf (ESAS) were analyzed. Positive matrix factorization (PMF) elucidated a mixed petrogenic and pyrogenic sources and distinct PAH fates associated with diverse input pathways including coastal permafrost erosion contribution (∼30%), petrogenic‐related emission (∼34%), fossil fuel combustion (∼26%), and biomass burning (∼10%). Correlation analysis indicated that BC plays a key role in affecting the behavior and fates of PAHs. In the Chukchi Sea, PAHs are closely associated with soot‐BC, whereas in the Laptev Sea (LS) and west East Siberian Sea (W‐ESS), they exhibit a coupling process with char‐BC. The presence of these carbonaceous materials in the sediments of CS is likely influenced by atmospheric deposition and biological activity, whereas the LS and W‐ESS regions are mainly affected by long‐distance river transport and direct deposition from coastal permafrost. As global warming continues, permafrost thawing induces the remobilization and retranslocation of PAHs, thereby becoming a significant PAH contributor and input pathway in the rapidly changing Arctic coastal margin.

Study of spatial spillover effects and threshold characteristics of the influence of urbanization on grain green production efficiency in China under carbon constraints.

Food security is a basic guarantee for maintaining social stability, and improving green and healthy grain production is the way to promote food security. China is currently experiencing rapid urbanization that has an impact on grain production and security, with reduced arable land, environmental pollution, insufficient agricultural population, and inadequate resource allocation. How to deal with the relationship between urbanization and grain production becomes the key to solve this problem. Therefore, this study constructs an evaluation system of grain green production efficiency (GGPE) with non-expected output containing carbon emissions, and uses the super-efficient SBM model to measure the level of GGPE, and constructs a spatial econometric model to examine the spatial correlation and spillover effect of urbanization on GGPE; then constructs a panel threshold model to analyze the nonlinear threshold characteristics between urbanization and GGPE. It was found that (1) from 2000 to 2019, China's GGPE showed an overall upward trend, and the performance of GGPE varied among different provinces. (2) According to the results of the spatial econometric model, China's GGPE shows obvious spatial characteristics, and the level of urbanization not only directly affects regional GGPE but also can indirectly affect neighboring regions' GGPE through spatial spillover effects, and the indirect effects among regions are larger than the direct effects. (3) Spatial threshold effect results, there is a significant non-linear threshold characteristic of the impact of urbanization rate on GGPE. In terms of the influence effects of other variables, the influence of each variable on GGPE is roughly in the same direction as the influence under the spatial spillover effect, but the degree of influence is slightly different.

Real-time pricing considering carbon constraints for smart grid with multiple energy sources

With the escalation of fossil fuel pollution and the challenges of climate change, the smart grid is forced to address the carbon emissions associated with fossil fuel use. Real-time pricing of smart grid is an effective means to regulate the balance of supply and demand in the grid as well as to achieve energy conservation and environmental protection. This paper takes traditional fossil energy and clean energy power supply as the background, takes social welfare maximization as the goal, combines carbon emission cost with traditional power generation cost, and constructs a real-time pricing model that takes into account carbon emission cost, so that thermal power enterprises participate in the power market and carbon market at the same time. By using duality methods, we transformed the original optimization problem into an equivalent one. Furthermore, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. The simulation results verify the feasibility and effectiveness of the proposed real-time pricing strategy.

Fall into the pseudo-decoupling trap: Type identification, trend characterization and solution path of carbon decoupling trap in urban agglomerations of China

This study investigates the stability and sustainability of carbon decoupling in urban agglomerations across China, where the strong coupling between economic growth and carbon emissions poses significant challenges. Despite efforts in energy conservation and emission reduction, urban agglomerations have seen unsatisfactory results. By analyzing the real-pseudo decoupling states in 19 urban agglomerations from 2007 to 2020, the objective of this study is to identify the type and trend characteristics of carbon decoupling traps and to propose solution paths for maintaining decoupling stability. Major Findings: (1) The decoupling state exhibits volatility and instability in urban agglomerations, making them susceptible to decoupling traps. (2) Most urban agglomerations remain un-decoupled, with a few cities achieving real decoupling and gradually shifted from northeast to southeast, while pseudo-decoupling and un-decoupled cities consistently cluster in the southwest and northwest regions. (3) Real-pseudo decoupling is driven by a combination of endogenous and exogenous factors, with energy structure, population density, and environmental regulation intensity emerging as pivotal influencers. (4) Geographical factors exhibit both commonalities and variations in their impact on real-pseudo decoupling. By identifying real-pseudo decoupling states and their driving factors, this study proposes strategic solution paths to overcome carbon constraints and achieve stable decoupling in urban agglomerations, contributing to the broader goals of sustainable economic and environmental development.

No pain, no gain? Simulation of carbon reduction potential and socioeconomic effects of voluntary carbon trading in China during 2021–2060

Voluntary carbon market has become a key mechanism of stimulating carbon emission mitigation actions globally. In this study, we calculate the emission reduction potential of Carbon Generalized System of Preferences (CGSP), a dominated voluntary carbon trading mechanism in China, including on-going projects of forestry carbon sinks, waste clothes reuse, installation of distributed photovoltaic power generation system, and usage of energy-conserving devices, as well as other extended methods for their future mitigation potentials. We employ a computable general equilibrium model to delve into the effects of CGSP mechanism on carbon price, economic growth and household consumption, assessing its impact on the equitability of social welfares between urban and rural residents during 2021–2060. Our results indicate that employing the current methods for CGSP would lead to around 5.0 Mt CO2 reduction annually. since 2018, whereas adopting the extended methods would yield reductions of 157.1 Mt. Promoting forestry carbon sinks and energy-conserving appliances offers the most prominent emission reduction, while low-carbon travel shows the biggest potential going forward. CGSP will loosen carbon constraints on industries in the market and result in a lower carbon price, reducing the economic cost of carbon trading without compromising China's carbon peaking and carbon neutrality goals. Supplementing CGSP could reduce the GDP loss by up to 0.2 trillion Yuan by 2030 and 0.9 trillion Yuan by 2060, compared to the sole operation of compliance carbon trading market. Provided fiscal stimulus policies, CGSP will further reduce GDP loss by 1.1 trillion Yuan in 2060. Different fiscal stimulus scenarios have a strong implication on welfares of urban and rural residents, wherein monetary equivalent incentive will reduce urban-rural income gap by up to 718 Yuan/capita in 2060.

Promoting Decarbonization in China: Revealing the Impact of Various Energy Policies on the Power Sector Based on a Coupled Model

The carbon emissions of the power industry account for over 50% of China’s total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China’s energy transition, economic development, and the power sector’s energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China’s energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector’s generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended.

A novel hydrogen supply chain optimization model – Case study of Texas and Louisiana

The increasing political momentum advocating for decarbonization efforts has led many governments around the world to unveil national hydrogen strategies. Hydrogen is viewed as a potential enabler of deep decarbonization, notably in hard-to-abate sectors such as the industry. A multi-modal, hourly resolved, linear programming model was developed to assess the infrastructure requirements of a low-carbon supply chain over a large region. It optimizes the deployment of infrastructure from 2025 up to 2050 by assessing four years: 2025, 2030, 2040, and 2050, and is location agnostic. The considered infrastructure encompasses several technologies for production, transmission, and storage.Model results illustrate supply chain requirements in Texas and Louisiana. Edge cases considering 100% electrolytic production were analyzed. Results show that by 2050, with an assumed industrial demand of 276 TWh/year, Texas and Louisiana would require 62 GW of electrolyzers, 102 GW of onshore wind, and 32 GW of solar panels. The resulting levelized cost of hydrogen totaled $5.6–6.3/kgH2 in 2025, decreasing to $3.2–3.5/kgH2 in 2050. Most of the electricity production occurs in Northwest Texas thanks to high capacity factors for both renewable technologies. Hydrogen is produced locally and transmitted through pipelines to demand centers around the Gulf Coast, instead of electricity being transmitted for electrolytic production co-located with demand. Large-scale hydrogen storage is highly beneficial in the system to provide buffer between varying electrolytic hydrogen production and constant industrial demand requirements. In a system without low-cost storage, liquid and compressed tanks are deployed, and there is a significant renewable capacity overbuild to ensure greater electrolyzer capacity factors, resulting in higher electricity curtailment.A system under carbon constraint sees the deployment of natural gas-derived hydrogen production. Lax carbon constraint target result in an important reliance on this production method due to its low cost, while stricter targets enforce a great share of electrolytic production.

Optimization of Urban Cold Chain Logistics Path Considering Carbon Constraints

Optimization of Urban Cold Chain Logistics Path Considering Carbon Constraints

Research on multi-time scale integrated energy scheduling optimization considering carbon constraints

To address the challenge of grasping carbon emission costs in different dispatching cycles under multiple time scales of the Integrated Energy System (IES), a carbon-constrained multi-time scale optimization method for integrated energy systems is proposed. First, a novel mechanism was proposed, Carbon Constraint Mechanism, which includes carbon tax, green certificate trading, and tiered carbon trading mechanism. Second, the carbon constraint mechanism was introduced into multi time scale scheduling optimization, and an IES multi time scale low-carbon optimization scheduling model was established. Third, the economic cost, carbon emissions, and primary energy consumption were considered as objective functions in multi-objective optimal scheduling. Moreover, utilized day ahead scheduling results and real-time load forecasting to dynamically adjust the output power of IES equipment in combination with power change penalties and carbon constraints. The simulation results showed that, compared to dispatch optimization strategies that solely prioritized economy, this study reduced carbon emissions by 28.38 % across multiple time scales while enhancing the economic performance of IES by considering carbon constraints. The multi-time scale dispatch optimization, incorporating carbon constraint mechanisms, improved the system's stability and low-carbon economy, proving the feasibility and effectiveness of the proposed optimal dispatch strategy.

Economic impacts of meeting China's NDC through carbon taxes with alternative schemes for recycling tax revenues

This study examines the impact of meeting China's Paris Climate Agreement commitment to reduce its emission intensity by 65 % below 2005 levels by 2030 through various policy instruments. We use a dynamic recursive computable general equilibrium model to simulate the economy-wide impacts of administrative mandates and carbon taxes using different revenue recycling schemes. China's GDP in 2030 would be approximately 1 % lower than the baseline if carbon emissions were constrained by mandates. The commitment's economic costs are lower than the carbon constraint case, regardless of revenue recycling. Economic costs are the lowest when carbon tax revenue is recycled to reduce taxes on capital or corporate income. Subsidizing solar and wind energy further reduces emissions, but at a higher economic cost with a GDP loss of about 0.53 %–0.58 % from the baseline. The results highlight the importance of proper design architecture to make carbon taxes more palatable to policymakers and taxpayers.

An optimal decision for fresh products' cold chain considering freshness and carbon emission reduction

PurposeThe aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.Design/methodology/approachThis study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.FindingsUnder the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.Originality/valueThis study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.

Future of hydrogen in the U.S. energy sector: MARKAL modeling results

Hydrogen is an attractive energy carrier which could play a role in decarbonizing process heat, power or transport applications. Though the U.S. already produces about 10 million metric tons of H2 (over 1 quadrillion BTUs or 1 % of the U.S. primary energy consumption), production technologies primarily use fossil fuels that release CO2, and the deployment of other, cleaner H2 production technologies is still in the very early stages in the U.S. This study explores (1) the level of current U.S. hydrogen production and demand, (2) the importance of hydrogen to accelerate a net-zero CO2 future, and (3) the challenges that must be overcome to make hydrogen an important part of the U.S. energy system. The study discusses four scenarios and hydrogen production has been shown to increase in the future, but this growth is not enough to establish a hydrogen economy. In this study, the characteristics of hydrogen technologies and their deployments in the long-term future are investigated using energy system model MARKAL. The effects of strong carbon constraints do not cause higher hydrogen demand but show a decrease in comparison to the business-as-usual scenario. Further, according to our modeling results, hydrogen grows only as a fuel for hard-to-decarbonize heavy-duty vehicles and is less competitive than other decarbonization solutions in the U.S. Without improvements in reducing the cost of electrolysis and increasing the performance of near-zero carbon technologies for hydrogen production, hydrogen will remain a niche player in the U.S. energy system in the long-term future. This article provides the reader with a comprehensive understanding of the role of hydrogen in the U.S. energy system, thereby explaining the long-term future projections.

Can the digitalization level of agriculture improve its ecological efficiency under carbon constraints: Evidence from China

The interplay between digitalization and economic development constitutes a pivotal global issue, yet empirical research on agricultural ecological efficiency in developing countries remains limited. This study initially establishes a measurement system and a comprehensive index for the level of agricultural digitalization. Subsequently, it delineates the relationship between agricultural digitalization level and agroecological efficiency using the spatial Durbin model, and ultimately explores the enhancing effect of agricultural digitalization level on agroecological efficiency using China as a case study. Research reveals that the agricultural ecological efficiency across the 31 mainland Chinese provinces demonstrates a generally linear upward trajectory, embodying both agglomeration and heterogeneity. The level of agricultural digitization exerts a significant, positive direct impact and facilitates a spatial spillover effect on agricultural ecological efficiency. Other control variables, such as financial support for agriculture and local economic development, impart a positive direct impact on regional agricultural ecological efficiency, while rural household operating income propels a positive spatial spillover effect on adjacent areas. The findings furnish guidance for developing countries to adeptly execute digital rural construction, aiming to enhance agricultural ecological efficiency amidst carbon constraints.

Evaluating and Analyzing the Efficiency and Influencing Factors of Cold Chain Logistics in China’s Major Urban Agglomerations under Carbon Constraints

With environmental degradation and energy shortages, green and low-carbon development has become an industry trend, especially in regards to cold chain logistics (CCL), where energy consumption and emissions are substantial. In this context, determining how to scientifically evaluate the cold chain logistics efficiency (CCLE) under carbon emission constraints is of great significance for achieving sustainable development. This study uses the three-stage data envelopment analysis (DEA) and the Malmquist index model to analyze the overall level and regional differences regarding CCLE in China’s four major urban agglomerations, under carbon constraints, from 2010 to 2020. Then, the influencing factors of CCLE are identified through Tobit regression. The results reveal that: (1) the CCLE in the four urban agglomerations is overestimated when carbon constraints are not considered; (2) the CCLE in the four urban agglomerations shows an upward trend from 2010 to 2020, with an average annual growth rate of 1.25% in regards to total factor productivity. However, there are significant spatial and temporal variations, with low-scale efficiency being the primary constraint. (3) Different influencing factors have different directions and exert different effects on CCLE in different urban agglomerations, and the improvement of economic development levels positively affects all regions.

An optimization strategy for intra-park integration trading considering energy storage and carbon emission constraints

Energy development in industrial parks promotes both industrial and inter-park economies. However, as the number of industrial parks increases and the economic level of the parks rises, energy consumption is also increasing rapidly. The rapid expansion of inter-park trading markets is accompanied by a surge in carbon emissions, making industrial parks increasingly significant contributors to carbon emissions within the industrial sector. In response to this environmental challenge, this paper proposes an integrated trading approach within industrial parks that considers carbon constraints. By implementing a spot trading method for surplus industrial energy, the goal is to mitigate the carbon emission pressure within these parks. Firstly, the paper conducts an analysis of the characteristics of combined heat and power units in industrial parks. It introduces an integrated analysis method within the target region, consolidating information from various park units to enhance flexibility in energy utilization. Secondly, an energy storage model is established. This model efficiently leverages energy storage capacity to balance fluctuations in energy supply and demand within industrial parks, thereby alleviating carbon emission pressure. Finally, the study and analysis of an industrial park in Liaoning Province were conducted using the Yalmip + Gurobi commercial software on the MATLAB platform. Compared with the traditional industrial park, the carbon dioxide emission of the industrial park is reduced by 91.49t, and the energy cost is saved by 11.2 million RMB. Numerical results from instances indicate that this method surpasses traditional trading approaches, aligning with the rigorous standards of scientific research.

Implications of the Carbon Border Adjustment Mechanism on South Korean industries: Challenges and policy recommendations

This study examines the effects of the European Union's Carbon Border Adjustment Mechanism (CBAM) on South Korean industries. Concerns about carbon leakage, where carbon emissions increase in countries or regions with less stringent climate policies, have grown recently. Specifically, firms strategically relocate their operations to countries with lower carbon costs, where they can produce goods more cheaply and without carbon constraints. CBAM is a carbon tariff on carbon intensive products imported by the European Union. While the CBAM has the potential to reduce global carbon emissions, it poses significant risks to South Korea, which relies heavily on exports of CBAM-affected products. This study provides an overview of the potential effects of the CBAM on industries in South Korea, applies event study analysis to quantitatively evaluate how the financial market has reacted to CBAM-related events. Based on the research findings, as the details of CBAM become clearer, there is a shift in perception from environmental goals to broader issues across industries. Building on this understanding, we discuss issues related to the CBAM enactment: firm-level carbon accounting, EC-certified accreditation institutions, and the carbon allowance price gap between the EU and South Korea. We propose necessary short-term and long-term policies to attenuate CBAM-related risks.

How can the Pearl River Delta urban agglomeration achieve the carbon peak target: Based on the perspective of an optimal stable economic growth path

The Pearl River Delta (PRD) has high concentrations of energy consumption and carbon emissions, and the conflict between its economic development and environmental protection is relatively prominent. Beginning from the ideal path of economic growth, this study proposes a framework for carbon emission prediction under the optimal economic growth path of the PRD via the cross-analysis of low-carbon economic analysis and artificial intelligence prediction. First, the generalized Divisia index method (GDIM) is used to decompose carbon emissions to quantify the contribution rate of each influencing factor. Then, the genetic algorithm (GA) is introduced to optimize the backpropagation (BP) neural network to construct a GA-BP combined prediction model. Finally, the optimal economic growth rate models under different scenarios are constructed, and the carbon peak in the PRD under stable economic growth is accurately predicted. The results show that under the optimal economic growth path, the carbon peak in the PRD under the scenario with carbon constraints will be reached in 2029, while that under the scenario without carbon constraints will only begin to appear around 2035. Thus, the achievement of the carbon peak target in the PRD still requires insisting on stringent carbon emission reduction measures.

Re-measurement and influencing factors of agricultural eco-efficiency under the ‘dual carbon’ target in China

Given that agriculture is both a carbon source and sink, the sustainability goals of carbon peaking and neutrality place high demands on the green and low-carbon agricultural development in China, and the exploration of a realistic path for a sustainable agricultural development is urgently needed. Under the above ‘dual carbon’ target, this study focused on the key issue of how to improve China's agricultural eco-efficiency (AEE) and constructed an innovative AEE indicator system that can reflect carbon constraint and coordinated agricultural economic development, resource use and ecological environment. The super-efficient slack-based measured Data Envelopment Analysis (SBM-DEA) method, which considers undesirable outputs, was applied to re-measure the AEE of 30 provinces and cities in China from 2001 to 2020, and its spatial and temporal evolution was analysed in conjunction with kernel density estimation. The Tobit regression model was used to explore various influencing factors by region. The results show that the AEE re-measurements, which take into account the 'dual carbon' requirement, are significantly better than the traditional AEE. From 2001 to 2020, China had an overall V-shaped fluctuation curve AEE, with a small decline and several inter-annual fluctuations, and exhibited a large potential to rise. China's AEE showed a spatially uneven regional development at different stages of distribution and evident multi-polar differentiation. Inter-provincial differences were observed in China's AEE, and the vicious circle of low-level green and low-carbon agricultural development was difficult to break. Urbanisation had a significant positive effect on national and eastern AEE but a significant negative effect on central AEE. The agricultural carbon offset rate had a significant effect on AEE nationally and in the three regions. Thus, the introduction of ‘dual carbon’ target effectively drove the development of AEE. Agricultural industry structure inhibited the improvement of AEE nationally and in the western region. Agricultural economic development hindered the national AEE improvement but promoted that of the central region, where China showed an environment Kuznets curve. Hopefully, this study can provide data support and theoretical reference for the green and low-carbon agricultural development and help achieve the ‘dual carbon’ target.

Identification of clean energy development routes under carbon emission constraints: A path towards structural adjustment of the power system

Clarifying the clean energy (CE) development path is significant for realizing energy system transformation, coping with climate risks, and ensuring energy and ecological security. This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints.

Carbon constraints and carbon emission reduction: An evolutionary game model within the energy-intensive sector

Energy-intensive enterprises form the backbone of low-carbon transformation in our society, which is attracting significant attention in terms of government policy-making. However, existing studies focus largely on the behavior of governments or enterprises in emission reduction; what is not asked—and therefore not explained—is the coordination mechanism between government and industry. More crucially, very few studies have taken the initial willingness of both governments and enterprises into consideration. The absence of that research may lead to a certain amount of conflict between economic development and environmental protection by local governments, leading to looser regulation. Meanwhile, energy-intensive enterprises would perceive local governments as tending toward laxity, further negatively affecting emission reduction. To help fill in these gaps in the literature, we build an evolutionary game model consisting of energy-intensive enterprises and the government. Specifically, we first construct a revenue matrix including governments and energy-intensive enterprises, and then analyze their evolutionary stabilization strategies in four cases: strict regulation with carbon emissions, strict regulation without carbon emissions, loose regulation with carbon emissions, and loose regulation without carbon emissions. It should be noted that the model is built with consideration of penalties levied by higher-level governments for lax regulation by local governments. Likewise, in the initial game state, the probabilities of energy-intensive enterprises taking the initiative to reduce emissions and strict government regulation represent the initial intention the respective parties. Finally, the dynamic evolution process is simulated numerically. Our main findings are as follows. 1) Government regulations and costs of reducing emissions are determinants of the equilibrium state in the game model. In the case of loose regulations or high emission reduction costs, both governments and enterprises embrace strategies with a negative effect on emission reduction. 2) Without effective external constraint mechanisms, both the government and enterprises would make decisions to maximize their own benefits. Doing so would lead to a sub-optimal equilibrium, impeding the achievement of ideal emission reduction goals. 3) The initial willingness of governments and enterprises determines emission-reduction paths. Likewise, the initial intention to engage in strict regulation by governments exerts a stronger constraint on enterprises. Therefore, to guarantee policy efficiency, governments need to implement strict regulations, decrease enterprises’ emission reduction costs, and enhance the initial willingness of both sides. Under these conditions, energy-intensive enterprises would be more likely to accomplish low-carbon transformation.