Ensuring the secure and stable operation of the power system has long been a focal point in China’s efforts to construct a modern electricity infrastructure and is indispensable for advancing energy transition and supporting high‐quality socio‐economic development. From the unique perspective of interregional power interconnections and under the assumption of reserved coal‐fired generation capacity, this paper develops a differential DebtRank model to simulate the systemic risks and contagion effects that each region may pose as a source of disturbance. In addition, by employing panel data from 30 Chinese provinces and municipalities (excluding Hong Kong, China; Macao, China; Taiwan, China; and Xizang) over the period 2016–2022, we empirically investigate the determinants and transmission mechanisms of systemic risk within regional power exchanges. Our finding indicates that the power exchange network exhibits substantial resilience, with nodes demonstrating strong interdependencies and complementarities. Using 2022 as an illustrative example, Inner Mongolia and Ningxia emerge as critical nodes, and interregional risk contagion primarily occurs within individual regional grids. When the country is divided by the “Hu Huanyong Line”, systemic risk is markedly higher in the northwest than in the southeast, and this disparity has been widening over time. Regions characterized by larger GDP, stricter environmental regulations, and more advanced transportation infrastructure experience lower systemic risk, whereas the scale of power generation and net export values serve as significant amplifiers of systemic risk. By proposing a novel analytical framework and enriching the existing literature on power‐sector risk prevention, this study offers theoretical support and policy recommendations for comprehensively enhancing the resilience of China’s power network in the face of global changes.
As the energy and power system undergoes a deep transition, the interdependence among generation, grid, load, and storage continues to strengthen, and operational uncertainty has become increasingly prominent. To identify potential risks in this transition process, this study develops a power system transition risk indicator system and evaluation model based on security, adequacy, and energy efficiency, covering generation-side output, grid-side transmission and distribution, load-side demand, and energy-storage regulation. A combined weighting mechanism is further constructed by integrating the G1 method and the cosine similarity method, and transition risks are measured for China’s six regional power grids. In addition, the Dagum Gini coefficient decomposition method is introduced to characterize the overall evolution of risk structures and intra-regional disparities across 30 Chinese provincial-level regions (excluding Hong Kong, China; Macao, China; Taiwan, China; and Xizang) from 2013 to 2022. Stage-specific dominant variables are then identified through risk-factor ranking. The results show that the overall national risk level declined during the study period, while load-side and storage-side risks exhibited strong volatility and marked stage-specific increases. Some provinces still face evident weaknesses in peak-shaving capacity, demand-response capability, and supporting mechanisms for energy storage, which constrain system stability. Policy environment, power generation efficiency, and interprovincial transmission capacity exert significant impacts on transition risks. Based on the observed risk characteristics and indicator evolution trends, this study proposes differentiated risk regulation strategies and system governance recommendations, thereby providing decision-making support for the secure transition toward a new power system and the modernization of governance capacity.
The virtual power plant (VPP) is a crucial component of the new electric system and one of the key features that distinguish it from conventional power systems. The VPP can dispatch energy storage to achieve load demand response. However, as customer-side storage is integrated at scale, private cost information becomes more difficult to verify, hindering effective dispatch and undermining economic performance. This paper takes into account the impact of asymmetric information to develop a two-stage optimal dispatch strategy for VPPs. In the first stage, we build a day-ahead optimization model formulated as a differential game theory and apply optimal control theory to determine storage sizing and the day-ahead dispatch plan. In the second stage, we construct a real-time optimization model based on mixed-integer nonlinear programming to obtain the dispatch of VPP. The results indicate that, relative to the symmetric-information benchmark, asymmetric information dampens the VPP’s incentive to deploy storage: Neglecting its impact would reduce storage capacity by 47.2%, raise costs to2.4 times the baseline, and decrease dispatch revenue by 8.2%. The proposed two-stage optimization significantly mitigates the impact of asymmetric information and increases dispatch revenue by 11.1%.
The coordination of profit allocation among market participants is critical to encouraging emerging entities to engage in ancillary service markets, where scientific and reasonable contract design plays a key role. This paper develops a transaction decision-making model of V2G (vehicle-to-grid) reserve ancillary services between the microgrid operators and the electric vehicle aggregator, and compares the impacts of different contract designs on their equilibrium decisions and profit allocation. The main findings are as follows: 1) The cost-sharing contract and the revenue-sharing contract only benefit one party, whereas a combined cost-and-revenue sharing contract can achieve win-win outcomes and fully effective coordination by setting the cost-sharing and revenue-sharing ratios properly. 2) The microgrid operator’s cost-sharing ratio depends solely on its bargaining power, while the revenue-sharing ratio is also influenced by subsidies, user responsiveness, the aggregator’s operating costs, and so on. 3) Under the decentralized decision-making or cost-sharing contract, high subsidy levels may induce the aggregator to accept “negative price” transactions in exchange for the microgrid’s call for V2G reserve ancillary services, thereby obtaining the subsidy-driven profits. 4) When the microgrid operator’s bargaining power is relatively strong, it promotes the voluntary adoption of a combined cost-and-revenue sharing contract by both parties, achieving effective coordination.
Under the carbon neutrality target, the large-scale deployment of electric vehicles presents new challenges to power system operation. Vehicle-to-grid interaction is regarded as a key pathway to achieve peak shaving and provide storage support. However, significant differences in user access times and charging preferences may lead to a mismatch between participation in vehicle-to-grid and grid regulation needs, affecting the effectiveness of load management and storage supply. To address this issue, this study develops a simulation model for vehicle-to-grid interaction pathways that integrates electric vehicle development trends with user behavior characteristics. Based on large-scale real charging data, typical charging behavior types are identified, and behavior-based vehicle-to-grid strategy scenarios are constructed. The model evaluates the impacts of these strategies on grid load regulation, storage supply potential, and the mitigation of demand for critical resources under the carbon neutrality pathway. Results show that vehicle-to-grid strategies with orderly charging can provide 600.7 GW of mobile storage accounting for 53.2 percent of the additional demand, and cumulatively alleviate 5188.4 kilotons of critical resource demand. This study reveals how behavioral differences influence the outcomes of vehicle-to-grid strategies and provides insights for optimizing vehicle-to-grid strategies under carbon neutrality goals.
With the continuing expansion of distributed energy resource integration in distribution networks and the rapid growth in the number of prosumers, peer-to-peer (P2P) energy trading has become an important means to enhance supply — demand flexibility and market efficiency. However, existing trading strategies generally fail to capture heterogeneity in prosumers’ resource endowments, trading preferences, and individualized bargaining behavior, and thus cannot meet personalized requirements. To address these issues, a game-theoretic collaborative strategy based on cooperative game theory for multi-prosumer P2P electricity trading and operational optimization is proposed. Building on Nash bargaining theory, a dynamically adjustable cooperative bargaining model among multiple prosumers is formulated. A two-stage decomposition is then adopted to coordinate decisions on traded quantities and prices, so that each prosumer’s internal supply-demand balance is maintained while external P2P trades proceed in an orderly and rational manner. In addition, Copula theory is employed to model the stochastic source-load dependence of prosumers, and K-means clustering is used for scenario reduction to obtain representative scenarios. Simulation results demonstrate that the proposed method effectively improves both the operational flexibility and the economic performance of individual prosumers, thereby validating the effectiveness of the model and the algorithm.
To support Chinese enterprises in reducing the electricity-related carbon footprint of their products and to empower the national ‘Dual Carbon’ goals, this paper investigates the investment and construction of‘wind-solar-storage’ microgrids from an operator’s perspective. Firstly, we construct a unit commitment output optimization model for the microgrid. Solved via mixed-integer programming, the results reveal that microgrid operating revenues exhibit significant volatility under the optimal capacity allocation of wind, solar, and storage. Secondly, accounting for uncertainties such as load, renewable generation output, investment costs, carbon prices, and green certificate prices, we evaluate a real options valuation model. The findings indicate that the zero-carbon scenario is less economically viable than the baseline scenario. Although expanding wind and solar capacity increases power generation and consequently enhances revenues from operations, green certificates, and carbon reductions, these financial gains are insufficient to offset the drawbacks incurred by higher investment costs and system restructuring. Thirdly, as installed capacity expands and the power supply structure evolves, reductions in investment costs and the dynamics of carbon and green certificate prices directly impact the investment value and optimal timing across both baseline and zero-carbon scenarios.
Energy storage can effectively alleviate peak shaving and frequency regulation pressure, as well as curtailment issues caused by large-scale integration of wind and photovoltaic power into the grid. However, in power systems with shared energy storage participating alongside wind and photovoltaic generation, effective cooperative operation modes among multiple entities and their benefit distribution mechanisms remain unclear. Based on the shared energy storage capacity configuration wind-solar-storage cooperative operation model proposed in this paper, we construct a two-layer optimization model for wind-solar-storage cooperative operation and benefit distribution. The upper-layer model maximizes the cooperative alliance’s benefits by optimizing electricity transaction volume decisions, while the lower-layer model employs Nash bargaining to determine internal transaction prices within the cooperative alliance and achieve fair benefit distribution. Additionally, K-means clustering is utilized to optimize energy storage capacity configuration across multiple wind and solar output scenarios throughout the year. Research results demonstrate that: Compared to non-cooperative modes, the wind-solar-storage cooperative operation model based on shared energy storage enhances the overall benefits of the cooperative alliance and reduces planning deviation costs; as more energy entities join, this cooperative model can reduce energy storage capacity requirements and strengthen cooperative advantages by optimizing power complementarity among entities; the introduction of shared energy storage significantly reduces the volatility of renewable energy generation and effectively eliminates severe deviation situations; across different wind and solar power generation scenarios, although energy storage capacity requirements vary significantly, the demand curves for energy storage capacity generally exhibit an inverted “duck curve” shape. Moreover, the trend of change remains highly similar across all six scenarios.
Under the systemic transition of energy structures, single market instruments struggle to coordinate the multidimensional objectives of green and low-carbon development, necessitating in-depth exploration of coordinated mechanisms among electricity, carbon, and green certificate markets. Considering the critical impact of market entities’ strategic interactions on market equilibrium, this study constructs a multi-agent heterogeneous behavioral strategy model based on cumulative prospect theory to characterize the decision-making logic of bounded rational market participants. Simultaneously, it proposes two coupling mechanisms for electricity-carbon-green certificate markets: A “top-down” policy-driven approach and a “bottom-up” market-driven approach. A bi-level trading model optimizing both agent utility and social welfare is developed, with dynamic adaptability between coupling mechanisms and different energy transition stages examined through multi-scenario simulation analysis. Key findings include: 1) The “top-down” mechanism effectively stabilizes prices and enhances social welfare during early transition phases through policy constraints, while the “bottom-up” mechanism demonstrates superior resource allocation efficiency via price signals in later stages; 2) renewable energy penetration evolution significantly affects green certificate and carbon quota balances across all mechanisms, requiring vigilance against liquidity risks in carbon and certificate markets during high-penetration phases; 3) market agents’ risk preferences exhibit phased differential impacts on environmental-economic effects, with decision-making dynamics shifting from risk-aversion dominance to marginal sensitivity variation as the energy transition progresses. These conclusions provide valuable references for coordinating electricity-carbon-green certificate market development across different energy transition stages.
The low-carbon transition of electricity is a critical pathway toward achieving China’s “dual-carbon” strategic objectives. Aiming at the renewable energy power penetration mechanisms taking into account consumer green preferences, this study develops a Stackelberg game model involving renewable power generators, thermal power generators, and consumers in electricity, carbon, and green certificate markets. Through deriving multi-agent trading strategies and analyzing renewable energy equilibrium dynamics, we reveal that high green certificate prices favor priority clearing of renewable power for greater renewable energy adoption, while low green certificate prices favor simultaneous clearing of renewable power and thermal power for greater renewable energy adoption. At moderate certificate prices, the carbon price becomes the decisive factor. Lower carbon prices make priority clearing preferable for greater renewable energy adoption, whereas higher carbon prices favor simultaneous clearing. Sensitivity analysis shows that both elevated carbon prices and stronger consumer green preferences consistently enhance renewable energy’s equilibrium quantity and market share. In addition, declining renewable generation costs further promote its market penetration. These findings systematically elucidate the driving mechanisms of renewable energy power penetration in electricity markets from the market perspective, offering evidence-based policy recommendations to facilitate an efficient and scientifically-grounded low-carbon transformation in the power sector.
As a key market-based environmental policy, China’s emissions trading scheme (ETS) is of critical importance for incentivizing corporate green technological innovation and achieving the “Dual Carbon” targets. This paper provides a firm-level evaluation of its effectiveness. By manually matching lists of regulated enterprises from China’s pilot carbon markets with data on green and low-carbon technology patents for publicly listed firms, we leverage the inclusion of firms into the ETS as a quasi-natural experiment to empirically examine its impact on innovation from 2007 to 2020. We find that the ETS significantly boosts the green and low-carbon technological innovation of regulated firms. This effect is more pronounced for carbon-reduction and zero-carbon technologies than for negative-emission technologies. Heterogeneity analyses confirm that this innovation-promoting effect varies across different technology types and firm characteristics. Crucially, our mechanism analysis reveals that the ETS does not operate by increasing firms’ R&D expenditures; rather, it enhances innovation performance by fostering collaborative innovation models. Furthermore, we find that moderate increases and fluctuations in carbon prices during the pilot phase incentivized firms to innovate, though the design of market mechanisms still requires optimization. By providing micro-level evidence from the perspective of corporate green innovation, this study offers important empirical insights for the construction of China’s national unified carbon market and the pursuit of its decarbonization goals.
Ecological environment governance faces multi-objective conflicts arising from system complexity and intergenerational mismatches of costs and benefits driven by cross-generational externalities. Accordingly, the current policy framework urgently requires the establishment of a synergistic mechanism that aligns carbon reduction, pollution abatement, ecological enhancement, and economic growth, while maintaining intergenerational equilibrium between short-term emission reduction and long-term structural transformation. This study incorporates an overlapping generations population structure into an economy-environment-climate dynamic stochastic general equilibrium model. The model is used to examine the interactive effects and intergenerational transmission mechanisms of carbon markets and environmental taxes, and to explore their joint optimization pathways under the objective of maximizing intergenerational welfare. The results indicate that: 1) The interaction between the carbon market and environmental tax produces a superimposed weakening effect, which is most pronounced when the tax rate exceeds 8.52 CNY/kg PM2.5. 2) The younger generation bears the governance costs, whereas the environmental and climate benefits are transmitted to future generations through intergenerational externalities. 3) The optimized policy portfolio achieves Pareto improvements, mitigates economic fluctuations, and ensures that by 2050, the global temperature increase remains below 2
Understanding the mechanism of low-carbon transition is essential for China’s dual carbon goals. This paper analyzes how carbon premium drives China’s energy and economic transformation. We develop a green growth model incorporating capital, fossil and clean energy, carbon stock, and social cost of carbon to examine the role of carbon premium in energy restructuring and low-carbon transition. Numerical simulations identify optimal pathways under carbon neutrality. Results show a three-phase transition driven by carbon premium dynamics: Initially, low resource and environmental costs sustain fossil energy’s advantage, supporting capital and fossil-fueled growth; then, rising resource scarcity and carbon accumulation activate carbon premium, enabling clean energy substitution and a transitional phase driven by all three factors; finally, escalating resource and environmental costs push fossil energy above clean alternatives via carbon premium, achieving carbon-neutral growth powered by capital and clean energy.
The restart of the CCER (China certified emission reduction) project marks the entry of China’s carbon market into the era of dual-wheel drive by carbon quota and CCER. This paper focuses on the new requirements and characteristics of “carbon quotas + CCER”, taking two supply chains composed of the emission reduction manufacturer, the retail platform, the controlled emission manufacturer, and the voluntary emission reduction enterprises as research objects. For the coexistence of inter-chain price competition and carbon reduction technology investment cooperation in the supply chain, from the perspective of stakeholders, a corresponding noncooperative-cooperative biform game model is established to study the optimization of inter-chain price competition and intra-chain carbon emission reduction technology investment cooperation strategies and profit allocation. The interaction mechanism between CCER trading price, products’ sales price competition strategies, and carbon emission reduction technology investment cooperation strategy is revealed. The results show that: 1) When the cost coefficient of carbon emission reduction technology investment, carbon quota trading prices, and other parameters satisfy certain conditions, the coexistence of price competition between supply chain chains and cooperation in carbon emission reduction technology investment of the chain can be formed. Moreover, cooperation between the emission reduction manufacturer and the retail platform is conducive to improving the carbon emission reduction technology investment level, and the cooperation coalition is stable; 2) as the complex competition and cooperation relationships in the supply chain, the emission reduction manufacturer and the emission control manufacturer should consider the impact of CCER investment costs and trading prices when optimizing carbon reduction technology investment levels and product sales prices. Moreover, relevant government departments should also pay attention to manufacturers’ carbon reduction investments and product pricing strategies when formulating CCER trading prices; 3) when the carbon quota trading price is high, implementing the “carbon quota + CCER” policy can simultaneously increase supply chain profits and effectively reduce supply chain carbon emissions. The research results can provide decision-making methods for relevant government departments to improve carbon trading mechanisms, supply chain production, sales, carbon reduction investments, etc., promote industry cost reduction and efficiency improvement, and achieve the “dual carbon” goals.
As intelligent manufacturing technologies-characterized by automation and digitalization-continue to proliferate, their emission-reduction effects in the context of industrial upgrading are becoming a key driver in achieving China’s “dual carbon” goals. This study takes the application of industrial robots as a representative case of how intelligent manufacturing facilitates low-carbon transformation. Using panel data from Chinese cities between 2010 and 2021, the paper systematically examines the impact of industrial robot adoption on urban carbon emissions and explores the underlying mechanisms. The results show that industrial robot applications, in terms of both total volume and density, significantly promote low-carbon urban transformation. These findings remain robust after multiple sensitivity and endogeneity tests. Further analysis reveals significant heterogeneity in these effects across cities with different levels of development and geographic characteristics. Mechanism analysis indicates that industrial robots contribute to carbon reduction primarily through three channels: Restructuring energy consumption patterns, improving energy efficiency, and optimizing industrial structures. The findings offer a comprehensive understanding of the emission-reduction potential of industrial robots and provide policy guidance and practical implications for synergistically advancing intelligent and green development to accelerate the realization of the “dual carbon” targets.
The 20th National Congress of the Communist Party of China explicitly proposed strategic initiatives to accelerate the green transformation of development models and synergistically advance carbon emission reduction and pollution control. While carbon emission reduction addresses climate change and pollution control achieves environmental governance, these two objectives share inherent consistency in promoting high-quality development yet exhibit significant differences in implementation priorities. The former possesses global and long-term characteristics, whereas the latter demonstrates local-specific features. As the micro-foundation of national economic development, enterprises inevitably display distinct strategic motivations in choosing between pollution control and carbon emission reduction. Understanding Chinese enterprises’ preference patterns and their impacts in this context holds critical significance for achieving synergistic governance. This study employs textual analysis techniques to first construct two attention indicators: Enterprise “low-carbon development” and “environmental pollution”, to characterize enterprises’ preference patterns. Subsequently, we empirically examine how differences in attention propensity affect corporate financial and environmental performance. Results demonstrate that attention to both dimensions positively facilitates performance, thereby validating the economic value of green development concepts. However, these effects operate through distinct channels: Attention to low-carbon development improves performance through promoting environmental investment activities, while attention to environmental pollution enhances performance via influencing financial investment strategies. Further analysis reveals that internal governance structures and external environmental regulations significantly shape enterprises’ attention propensities toward carbon emission reduction and pollution control, ultimately reflecting in their financial and environmental performance. This research provides scientific evidence for understanding enterprise green development patterns, offers novel insights for deepening synergistic enhancement of carbon mitigation and pollution reduction in China, and holds important reference value for advancing high-quality economic development and environmental sustainability.
To reveal the influence of strategic behavior rules, network environment and market incentives on the diffusion of green innovation, this paper breaks through the traditional assumption of homogeneity, and based on the heterogeneous network of firms with different node attributes, green innovation is regarded as the strategic choice of firms, and an evolutionary game model of the diffusion of green innovation of firms is constructed, and the green innovation of the iron and steel industry is used as a case study for the simulation and analysis. It is found that the green innovation diffusion trajectories of large enterprises and small and medium-sized enterprises show non-equilibrium characteristics, the green innovation diffusion rate of small and medium-sized enterprises is faster. The innovation decisions of enterprises are influenced by their local network environments, and the networks of heterogeneous enterprise accelerates the green innovation diffusion process of large enterprises. Meanwhile, due to the heterogeneity of firms, the global decision-making mechanism based on the combination of individual best-response dynamics and dynamic specification has a positive effect on promoting the diffusion of green innovations in large firms. For small and medium-sized enterprises, this mechanism can promote the rapid diffusion of innovations in the early stage of innovation diffusion, while in the middle and late stages, the comparative imitation behavior among peers becomes an important mechanism to realize the diffusion of innovations on a large scale. In addition, dynamic subsidies are better than static subsidies in promoting the diffusion of green innovations, and the increase of carbon trading price also helps to accelerate the innovation diffusion. This study provides theoretical support for understanding the diffusion mechanism of green innovation and expands the application of complex network theory in green innovation research.
In the context of China’s carbon peaking and carbon neutrality strategy, understanding the cross-regional and cross-sectoral diffusion mechanisms of carbon-neutral generic technologies is crucial. This paper constructs a region-industry level technology network based on patent citations and analyzes its structural characteristics using social network analysis. We further apply a temporal exponential random graph model to identify the endogenous mechanisms shaping network formation. The results reveal a “small-world” structure, with diffusion pathways shifting from a few core nodes to locally clustered subgroups, indicating a trend toward decentralization. Motif analysis shows a transition from clustering toward reciprocity and interconnection, reflecting stronger cross-industry collaboration. Mechanism analysis indicates that mutual, triangle-closing structures and the presence of a few “star” nodes significantly promote network formation — Findings that remain robust to controls for node attributes, time effects, and exogenous network structures. This study provides empirical insights into the evolution of carbon-neutral generic technology networks and offers guidance for fostering cross-regional and cross-industry collaborative innovation and optimizing low-carbon technology diffusion pathways.
As a foundational vehicle for developing new productive forces, new infrastructure integrates technological drivers with green and low-carbon attributes, playing a critical role in advancing high-quality development and achieving the “dual carbon” goals. Based on a sample of 279 Chinese cities, this study constructs a model to assess the impact of new infrastructure construction (new infrastructure) on the synergistic effect of pollution and carbon reduction, evaluating its synergistic effects and identifying potential mechanisms. Our study finds that new infrastructure significantly promotes the synergistic effect of pollution and carbon reduction. This conclusion holds even after a series of robustness and endogeneity tests. The synergistic effects of new infrastructure are more pronounced in growth-oriented resource cities, medium and small cities, and old industrial bases. New infrastructure can promote synergistic pollution and carbon reduction through green technological innovation, energy transformation, industrial upgrading, and public environmental awareness. In specific sectors, power infrastructure development, transportation infrastructure development, and digital infrastructure development can all promote the synergistic reduction of pollution and carbon emissions, but they differ in their pathways of impact. These findings not only enrich the theoretical framework of synergistic pollution and carbon reduction but also provide scientific evidence for improving new infrastructure policies.
To achieve the dual-carbon goals while sustaining industrial development, the Chinese government, amid a policy shift from energy consumption control to carbon emissions control, promotes clean energy deployment in western regions to reduce both energy costs and carbon emissions, and encourages the relocation of energy-intensive industries from the east to the west. However, the relocation of an individual manufacturer alone is insufficient to maintain overall competitiveness; the manufacturer moving west still requires the coordinated participation of supply chain partners to form industrial clusters. Therefore, it is crucial to examine the driving forces and conditions under which related firms are willing to relocate. Building on this, this paper develops a four-stage game model involving a brand owner and multiple suppliers of complementary components. The main findings are as follows: 1) As more eastern suppliers relocate, the total economic benefit (i.e., the total profit of both clusters) first increases and then decreases, while the total carbon reduction benefit (i.e., total carbon emission reductions)follows an opposite trend. 2) There exists an optimal number of supplier relocations that maximizes the joint economic-carbon reduction benefit, and government policies that reduce the fixed costs of relocation facilitate reaching this optimal level. 3) Retaining a reasonable number of suppliers in the east can improve the supply chain’s ability to cope with disruption risks under carbon regulations. While encouraging the relocation of energy-intensive suppliers, the government should also simultaneously promote low-carbon energy development and supply chain cluster formation in western regions.
This paper develops a nested multi-regional input output (MRIO) model integrating 31 Chinese provinces with major global economies and designs three scenarios: “International decoupling”, “Domestic circulation”, and “Combined relocation”. From a climate change perspective, this study provides the first systematic assessment of the carbon burden and underlying drivers of industrial relocation in the context of China-U.S. decoupling. The findings reveal that: 1) Global carbon responsibility is highly asymmetric-China’s production-based emissions far exceed its consumption-based, whereas developed economies such as the U.S., Europe, and Japan exhibit the opposite pattern. Nearly 80% of Chinese provinces generate more production-based emissions than consumption-based emissions in trade with the world’s major economies; 2) total global emissions rise under all scenarios, with the magnitude of the rise positively correlated with the degree of decoupling: The largest increase occurs under the international decoupling scenario (0.20%), followed by dual transfer(0.17%) and domestic circulation (0.14%). Consumption-based emissions in both China and the U.S. increase across all scenarios, while production-based changes vary significantly between the two; 3) the rise in emissions is primarily driven by two mechanisms: The carbon intensity effect from relocating industries to regions with lower carbon efficiency, and the intermediate input effect resulting from higher consumption of intermediate goods due to reduced production efficiency.
Assessing pollution and carbon reduction co-governance in the steel industry, and exploring the development of collaborative governance pathways, is essential to its high-quality and sustainable development. This study proposes a three-phase framework that integrates scenario optimization, key process identification, and pathway design for co-governance pathway. Using system dynamics modeling, this study simulate five scenarios:Business as usual (BAU), electric arc furnace production structure improvement (ESI), energy substitution (ESB), energy conservation and emission reduction technologies implementation (EEI), and integrated governance (ITG). Two composite indicators, namely pollution and carbon reduction index and synergistic emission reduction level, are constructed to measure CO2 and PM2.5 reductions and their synergy level, to compare various scenarios. The study further identifies key processes and evaluates the impacts of differentiated governance measures, proposing a co-governance pathway for pollution and carbon reduction in the steel industry. Simulations of the Beijing-Tianjin-Hebei (BTH) steel industry, the results show that integrating production structure improvement, clean energy substitution, and reduction technologies implementation measures (ITG) works best-considering reduction, synergy, and cost. The electric arc furnace process is key to enhancing the pollution and carbon reduction index, while coking, pelletizing, blast furnace, and basic oxygen furnace processes are vital for synergistic emission reduction level. Finally, this study proposes a four-stage co-governance pathway for the steel industry from 2020 to 2060 years, including the introduction, growth, maturity, and stabilization.
Against the backdrop of the ongoing “dual carbon” strategy, resource-based urban agglomerations are facing a dual challenge of structural transition pressure and practical difficulties in implementation during the process of promoting carbon emission reduction. This study takes the Hohhot-Baotou-Ordos-Yulin urban cluster as the research object and conducts a systematic analysis from three perspectives: Emission estimation, trend evaluation, and policy response. Firstly, using multi-source data from 2005 to 2023, the study applies machine learning methods to reconstruct historical city-level carbon emission series by integrating remote sensing indicators and statistical information. On this basis, a system dynamics model reflecting changes in industrial structure and energy consumption is developed to assess the evolution of carbon emissions from 2025 to 2060 under different development scenarios. Further, under defined policy constraints, three types of actors: Governments, state-owned enterprises, and private enterprises, are introduced to build a strategy selection model, analyzing the impact of policy parameter variations on emission reduction outcomes. Simulation results indicate that when carbon prices, penalties, and subsidies are set at moderate levels, the urban agglomeration can achieve 10%–15%remaining within a controllable range. However, under strong intervention scenarios, although emission reductions become more pronounced, the rapid increase in cost leads to higher uncertainty in policy implementation. Overall, policy design should maintain an appropriate balance between emission reduction effectiveness and social cost, while taking into account strategy deviations caused by differences among actors.
Carbon capture, utilization, and storage (CCUS) technology is a critical technical option for achieving net-zero emissions in the power and industrial sectors, and is of great importance to China’s carbon neutrality goals. Currently, the deployment of CCUS projects in China has entered a multi-sector phase, with projects spanning various industries including power, steel, cement, and chemicals. As the overall deployment scale continues to grow annually, competition among different sectors for limited storage resources is expected to intensify in the future. This study constructs a multi-sector CCUS source-sink matching optimization model based on carbon sources from four high-emission sectors — Power, cement, steel, and coal chemical and integrated onshore-offshore storage sinks. It also conducts a comparative analysis of how multi-sector competition and carbon neutrality goals influence CCUS deployment. The results show that multi-sector competition significantly reshapes CCUS deployment, increasing the average source-sink matching distance by 27.8% under a general mitigation scenario, and resulting in notable increases in both cross-provincial CO2 transport and offshore storage volumes. Under the carbon neutrality scenario, multi-sector competition reduces the average source-sink distance by24.8%. From a cost perspective, neglecting multi-sector competition weakens the competitive allocation of storage resources among facilities, thereby underestimating abatement costs. Specifically, average abatement costs increase by 184 yuan/t and 111 yuan/t under the general mitigation and carbon neutrality scenarios, respectively. These findings provide quantitative insights to inform China’s low-carbon transition in the power and industrial sectors and support the large-scale deployment of CCUS technology.
