With the deepening of high-quality development, China's economic growth is shifting from a resource-driven mode to the one led by the social innovation and efficiency. Water-use efficiency has become an important indicator for assessing the level of green development and determines whether economic growth can shift from high consumption to high efficiency under resource constraints. Improving water-use efficiency helps reduce water consumption per unit of output, thereby alleviating the dependence of economic growth on water resources. Guangdong Province, one of the fastest-growing and most open regions in China, ensuring the coordination between economic growth and water use has become an urgent priority for it. Therefore, this study explores how water consumption and economic growth are decoupling over time across Guangdong's cities, and what drives this process. Such exploration deepens understanding of economic and water resource coordination. It also provides references for building a water-saving society and optimizing water allocation. Using panel data on economic development and water consumption for 21 prefecture-level cities in Guangdong Province, China, spanning the period 2003—2024 and derived from the Guangdong Water Resource Bulletin and the Guangdong Statistical Yearbook, this study applies the Tapio decoupling index and the Logarithmic Mean Divisia Index (LMDI) model to discover the temporal evolution of the decoupling relationship between economic growth and water consumption, as well as to identify the key driving factors underlying these patterns. The results show:①Overall, Guangdong Province shows a favorable trend in decoupling economic growth from water consumption over the study period, indicating that economic expansion has increasingly relied on improvements in water use efficiency rather than proportional increases in water demand. Nevertheless, pronounced regional disparities persist across cities. In particular, highly industrialized and rapidly urbanizing cities such as Shenzhen and Dongguan have experienced a gradual deterioration in the decoupling relationship in recent years, suggesting that intensified economic activities and rising resource pressures may offset efficiency gains in certain stages of development.② The primary industry maintained 'strong decoupling' throughout the study period and remained stable. The secondary industry shifted from 'weak' to 'strong decoupling' with water consumption in 2008, peaking in 2020. This shift highlights the role of industrial restructuring and technological progress in mitigating water consumption associated with manufacturing growth. In contrast, the tertiary industry was considerably volatile, suggesting a relatively higher sensitivity of water demand in service-oriented sectors to short-term economic fluctuations and structural changes. ③Decomposition results further reveal that economic scale expansion and population effects are the principal drivers of increases in total water consumption. Whereas improvements in water use efficiency in the primary and secondary industries serve as the main forces constraining water demand. However, owing to differences in regional economic development patterns and water resource endowments, the relative importance of these driving factors varies markedly across regions and development stages. These inter-city differences and temporal heterogeneity underscore the need to consider local conditions when interpreting decoupling outcomes and assessing the sustainability of economic-water relationships.

Misaligned investment between renewable generation and enabling technologies undermines a just energy transition. Here, we develop a machine-learning optimization framework to quantify how five enabling technologies shape three transition objectives-decarbonization, equity, and resilience-and to identify an efficiency-maximizing allocation. Our analysis reveals that investment distribution is decoupled from transition performance. Underfunded hydrogen infrastructure and energy storage drive three-dimensional progress, whereas heavily capitalized power grids and electrified transport alongside underfunded carbon capture, utilization, and storage (CCUS) impede decarbonization by locking in carbon-intensive systems. This imbalance stems from narrow climate policies that cause insufficient funding for enabling technology and a concentration of capital in short-term storage and grid expansion. We propose raising hydrogen infrastructure and storage to 6% of renewable investment and reallocating overcapitalized power grids and electrified transport surplus (96.77%) to CCUS (5.56%) and advanced storage (21.16%). This strategy strengthens technology synergies and provides intervention pathways to unlock just-transition potential.

Carbon capture, utilization, and storage (CCUS) technology is pivotal in climate mitigation but lags behind expectations. This investigation employs machine learning methods grounded in collaborative governance theory to analyze multiscale drivers and development models in global CCUS deployment. We observe a policy-driven predominant paradigm, with cost barriers significantly impeding economies of scale. Hierarchical clustering reveals three distinct typologies, coordinative, single-axis and constrained models, that illustrate a Matthew Effect, characterized by “major-power dominance and minor-nation catch-up”. Crucially, the Gini coefficient for CCUS development inequality persists at 0.70–0.84, exhibiting tripartite asymmetry through policy convergence, cost equilibrium, and technological agglomeration, alongside emergent spatial counter-agglomeration trends in recent years. Counterfactual analysis indicates that a comprehensive optimized strategy could boost historical growth by 22.7% and double capture scale by 2030. Nevertheless, a persistent one-third deficit in meeting climate targets underscores the urgency for multilateral governance mechanisms to implement more aggressive global actions.

The proliferation of retired electric vehicle batteries presents a significant threat to environmental and resource sustainability, necessitating collaborative efforts among supply and recycling chain actors. This study examines how coalition cooperation among actors can enhance the recycling of electric vehicle batteries, thereby improving both economic and environmental performance. Specifically, we utilize non-cooperative game theory to analyse five cooperative strategies in a tripartite closed-loop supply chain involving an electric vehicle manufacturer, a retailer, and a third-party recycler. We also scrutinize the effects of joint recycling regulation and carbon policy, i.e., reward-penalty and cap-and-trade mechanisms, on supply chain operations. The findings indicate that augmented external environmental pressures, in addition to enhancing collection rates, may bolster the supply chain’s economic yield under specific scenarios. While cooperative strategies led by an electric vehicle manufacturer exhibit potential for superior economic viability or heightened environmental sustainability, the formation of partial coalitions within the supply chain could result in an unequal distribution of profits. We employ cooperative game theory, specifically the Shapley value, to implement a fair profit allocation mechanism aimed at fostering cooperation and coordination within the supply chain.

Groundwater is the world's largest freshwater resource after ice caps and glaciers, and its over-exploitation can disrupt regional hydrological cycles, leading to issues such as land subsidence and salinization. Identifying hotspots and drivers of groundwater storage changes is essential for sustainable water management and climate change mitigation. This study uses GRACE/GRACE-FO satellite data to identify groundwater storage change hotspots in mainland China over the past two decades, employing Pettitt-test and temporal stability analyses. To ensure reliability, we cross-validated the GRACE/GRACE-FO-derived groundwater storage against Watergap Global Hydrological Model and available well records, the correlation coefficient distribution is 0.76-0.88. The hotspots are categorized into loss (I, II, III) and gain (IV, V) categories. The severity of both gain and loss conditions increases with the level. Additionally, the study quantifies the contributions of natural and anthropogenic factors by integrating climatic and socio-economic variables. The results indicate that loss hotspots dominate in North China, Loess Plateau, Northwest China, Northeast China, and Qinghai-Tibet Plateau. In the Qinghai-Tibet Plateau region, the combined proportion of Level I, II, and III loss hotspots exceeds 60%, whereas in other regions, the combined proportion of these loss hotspots is over 75%. In contrast, surplus hotspots are prevalent in South China, Ch-Yu region, Middle-Lower Yangtze River, and Yun-Gui Plateau, where level IV and V gain hotspots exceed 60%. Groundwater changes in the Qinghai-Tibet Plateau and Loess Plateau are primarily influenced by land use, whereas economic factors play a more significant role in other regions. This study offers valuable insights into regional groundwater changes across China and provides a scientific foundation for effective water resource management.

Identifying the development of the digital economy is crucial for promoting digital innovation in China, achieving internet connectivity, and enhancing the country’s overall strength. To this end, this study first constructs an evaluation index system for digital economy development, comprising 15 indicators across three levels. Next, the Analytic Hierarchy Process (AHP) is employed to assign weights to these indicators. Finally, the Fuzzy Comprehensive Evaluation Method is used to measure and compare the digital economy development scores of provinces and cities in the Yangtze River mid-lower reaches, aiming to explore the key factors influencing digital economy development. The findings reveal that the R&D expenditure accounts for GDP proportion and science and technology expenditure are significant indicators affecting digital economy growth. Zhejiang Province has the highest level of digital economy development, while Jiangxi Province has the lowest. Based on these findings, suggestions are made to strengthen the digital foundation and increase investment in innovation.

Methane (CH4), the second-largest global greenhouse gas and a key driver of tropospheric ozone formation, critically influences climate change and air quality. As the world’s largest CH4 emitter, China must develop targeted mitigation strategies to support its carbon peak and neutrality goals while reducing ozone pollution. Here, we analyzed the spatiotemporal evolution of provincial CH4 emissions in China from 2000 to 2023 using spatial autocorrelation, hotspot detection, trend analysis, and K-means clustering. Our results revealed a triphasic emission trajectory—rapid growth followed by stabilization and a recent resurgence—with all provinces except Tibet showing increasing trends. The energy sector emerged as the primary contributor, particularly in Inner Mongolia, Shanxi, and Shaanxi, whereas agricultural emissions dominated in pastoral regions, such as Inner Mongolia and Sichuan, and rice-growing areas, such as Hunan and Hubei. Coastal provinces, including Shandong, Jiangsu, and Guangdong, exhibited waste disposal as their predominant CH4 source. Based on these patterns, we classified the emission zones into four distinct typologies: coal-dominant, waste-dominant, oil-agriculture composite, and multifactorial systems, proposing tailored mitigation frameworks that integrate CH4 and ozone co-reduction. This study provides a spatially resolved foundation for synergistic climate and air quality governance in China.

Clarifying the mechanisms through which coal mining affects groundwater storage (GWS) variations is crucial for water resource conservation and sustainable development. The Ordos Mining Region in China, a key energy base in China with significant strategic importance, has undergone intensive coal mining activities that have substantially disrupted regional groundwater circulation. This study integrated data from the Gravity Recovery and Climate Experiment Satellite (GRACE) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models, combined with weighted downscaling methodology and water balance principles, to reconstruct high-resolution (0.01°) terrestrial water storage (TWS) and GWS changes in the Ordos Mining Region, China from April 2002 to December 2021. The accuracy of GWS variations were validated through pumping test measurements. Subsequently, Geodetector analysis was implemented to quantify the contributions of natural and anthropogenic factors to groundwater storage dynamics. Key findings include: 1) TWS in the study area showed a fluctuating but overall decreasing trend, with a total reduction of 8901.11 mm during study period. The most significant annual decrease occurred in 2021, reaching 1696.77 mm. 2) GWS exhibited an accelerated decline, with an average annual change rate of 44.35 mm/yr, totaling a decrease of 887.05 mm. The lowest annual ground-water storage level was recorded in 2020, reaching 185.69 mm. 3) Precipitation (PRE) contributed the most to GWS variation (q = 0.52), followed by coal mining water consumption (MWS) (q = 0.41). The interaction between PRE and MWS exhibited a nonlinear enhancement effect on GWS changes (0.54). The synergistic effect of natural hydrological factors has a great influence on the change of GWS, but coal mining water consumption will continue to reduce GWS. These findings provide critical references for the management and regulation of groundwater resource in mining regions.

This study examines decision-making in closed-loop supply chains (CLSCs) for power batteries with blockchain-enabled traceability. A bidirectional game-theoretic model in which one traceability level links the forward (sales) and reverse (recovery) sides. The manufacturer leads in a game; the retailer sets price and collection; contract terms are chosen by Nash bargaining. We analyse two implementable mechanisms: R&D cost sharing and sales revenue sharing. The findings show that blockchain supports modest price pass through while increasing market demand and consumer surplus. When traceability costs are high, R&D cost-sharing and sales revenue sharing improve coordination. Bargaining power matters: greater manufacturer power raises feasible shares, while the optimal cost- and revenue-sharing rates decline as price sensitivity and traceability costs rise. These insights provide practical guidance for deploying blockchain in battery CLSCs and support the sustainable growth of the EV industry.

This study examines decision-making challenges in closed-loop supply chains (CLSCs) for power batteries, empowered by blockchain technology. A bi-directional recycling game model is developed to assess how blockchain-driven contractual coordination impacts CLSC performance. The findings show that blockchain adoption effectively enhances retail prices, market demand. When information traceability costs are high, R&D cost-sharing and sales revenue-sharing contracts improve supply chain coordination. Furthermore, we explore the impact of member bargaining games on supply chain coordination; it is found that manufacturers' bargaining power influences the optimal cost-sharing, which are negatively correlated with price sensitivity and traceability costs. These results provide useful insights and practical guidance for applying blockchain in CLSCs for power batteries, contributing to the sustainable development of the electric vehicle industry.

Carbon capture, utilization and storage (CCUS), a water resource-intensive technology, has been projected to be restricted by water resources in the future. However, the water-saving effects resulting from technology progress and CO2-Enhanced water recovery (CO2-EWR) process are not been fully considered. Based on the learning curve theory, this study proposes a new water accounting system for CCUS retrofitting of coal-fired power plants (CFPPs) from a dynamic perspective considering power generation, carbon capture, and CO2-EWR technology. The results show that the total water withdrawal per unit of CCUS retrofitting with the first-generation capture technology will decrease by 66.89 %, and the total water consumption per unit of that will decrease by 74.70 % from 2021 to 2050. By contrast, the water withdrawal and consumption per unit with the second-generation capture technology will decrease by 74.12 % and 82.51 %, with the overall unit water consumption below 0.5 m(3)/MWh. Moreover, the water usage caused by CCUS retrofitting will be greatly relieved through the progress of geological sequestration technology (especially the CO2-EWR). In addition, the water usage during the capture process for circulation cooling and air-cooling would present an "inverted U" shaped trend affected by the capture scale and technology progress. Remarkably, the water withdrawal and consumption intensity of CFPPs with air-cooling technology is significantly lower than those of the circulation cooling technology for power generation, while the opposite happens if the capture technology is applied. Even so, CCUS retrofitting for the CFPPs with air-cooling technology can save 60 x 10(4) m(3) of water resource after 2040, which is positive for the CCUS deployment in the arid areas of China.

The mismatch between trade-embodied economic benefits and CO2 emissions gives rise to inherent inequality within the era of global value chains (GVCs), whereas the existing accounting framework failed to distinguish between multinational corporations and local firms. Taking China as a case study, this study developed a novel Inter-country Input-output (ICIO) model based on the trade in factor income (TiFI) principle, while considering corporate heterogeneity. The findings reveal that when bilateral trades are measured in terms of trade in traditional value added (TiVA) principle, China's trade surplus with five partners was overestimated by 61 % from 2010 to 2015, with the United States being particularly overvalued by nearly 45 %. At the industry level, the Non-metallic manufacturing and Retail sectors accounted for over 10 % of China's exports, surpassing TiVA in terms of market share, indicating that China's contribution to crucial export sectors is underestimated. However, the evaluation based on the TiFI principle revealed a 27 % reduction in the overall carbon deficit, indicating that China's contribution to global carbon emissions continued to decline. The carbon-economic inequality (CEI) index between China and its trading partners indicates a value greater than or equal to 1, implying that China has encountered setbacks in global carbon trading. Notably, the application of the TiVA principle results in an underestimation of CEI between China-United States and China-South Korea, with respective values ranging from -0.13 to 0.27 and - 0.05 to 0.05. Overall, this study offers valuable scientific evidence towards addressing accountability and promoting climate justice.

Digitalization has unfolded great opportunities for its ability to promote carbon neutrality. Nevertheless, it is still in a nascent stage enduring uncertainties due to the lack of clear guidance about holistic digitalization contributions to the carbon neutrality process (CNP). Moreover, the intricate environmental effects provided by digitalization and digital mechanisms towards CNP present a fragmented landscape with controversial arguments, deserving further exploration. Addressing this gap, multidimensional macro environmental and economic impacts induced by digitalization - such as direct and indirect effects, rebound effects, spillover effects and higher-order effects - are systematically evaluated to answer the issues about whether digitalization can contribute to carbon neutrality. Furthermore, the synergetic management of digital application and carbon reduction are closely related to the energy value chain. To this end, by overviewing objective and comprehensive scenarios related to state-of-the-art digital technologies in multiple sectors of the energy value chain, this study proposes an integral mechanism framework by theorizing an undefined field about digitalization towards CNP in terms of sectoral innovation, energy systems, individual behaviours, carbon-related assurance, and energy security guarantees. Accordingly, this study also contributes to potential directions and rising policy expectations about further estimation of digital effects, broader digital scenarios towards CNP and sustainable digitalization.

Households have emerged as one of the primary sources for carbon emissions in China, thus posing challenges to the “dual carbon” objectives. Digital finance, an emergent form of industry that fused advanced technology with financial services, had a pronounced impact on household carbon emissions stemming from daily consumption. However, the mechanisms driving this impact have not been adequately examined. Based on micro-level household survey data across 25 Chinese provinces from 2012, 2014, 2016, and 2018, the study identified the chief channels via which digital finance affected household carbon emissions, deriving several key findings. First, digital finance augmented household carbon emissions, presenting a significant negative impact on the climate. Second, due to the existence of “digital divide” between rural and urban areas, the impact of digital finance was more subdued in rural areas. Additionally, the effects of digital finance were more pronounced in the affluent eastern provinces. Third, income mobility obscured the positive relationship between digital finance and household carbon emissions. This is primarily attributed to the urban-rural divide in China; taking into account that urban-to-rural transfers make income distribution more equitable, there is a counterintuitive drop in per capita consumption, thereby suppressing consumption-related carbon emissions. This presented the conundrum of “income distribution equality—consumption negativity”. Finally, financial literacy was identified as a crucial positive moderating role, enabling households with high financial literacy to harness the dividends of digital finance, thereby engaging in more diversified consumption activities and intensifying the negative impact of digital finance on carbon emissions. The findings reinforced the pivotal role of digital finance in bolstering efforts to combat climate change and ensuring environmentally-responsible economic advancements.

The potential of carbon capture, utilization and storage (CCUS) has been widely discussed worldwide, while the dynamic changing process, lock-in risk and water resource constraints towards carbon neutrality target are not fully considered in the previous studies. This study developed a comprehensive approach to optimize carbon mitigation potential, total capital expenditure and water resource stress for China's coal-fired power plants (CFPPs) with CCUS retrofitting in a dynamic environment. We found that the annual capture scale will start to increase significantly until 2030 and reach 2.4 billion tons CO2 in 2050. The newly-added capture scale of first-generation technology will decline gradually after 2030 due to the breakthrough in second-generation technology. Inner Mongolia, Jiangsu and Guangdong have the greatest potential for implementing CCUS projects, and the least in Hainan, Sichuan and Qinghai. The annual capture cost roughly presents an "inverted U" shape with the peaking (14.70 billion CNY) occurring in 2035, while the annual R&D investment can be observed a moderate "N" shape with the peaking (11.24 billion CNY) occurring in 2030. The annual non-corporate expenditures (subsidy) will picture a significant "inverted U" type trend, peaking at 63.71 billion CNY in 2044 as a result of the risk factors such as CCUS facility investment and additional storage cost caused by uncertain geological conditions. In addition, the annual water withdrawal and consumption of capture process will increase from 0.09 and 0.06 billion m3 respectively in 2021 to 9.12 and 6.22 billion m3 respectively in 2050, while CO2-EWR (Enhanced water recovery) process will make CCUS technology supply extra water resource since 2035, reaching 27.05 billion m3 in 2050. Meanwhile, in terms of first- and second-generation capture technology, the unit water withdrawal will drop 35.04% and 36.71%, respectively, while the unit water consumption will drop 66.89% and 74.12%, respectively during 2021–2050. Overall, although CCUS will not intensify water resource stress in the future, the appropriate relaxation ratio of water quota is essential at the initial stage.

Due to the barriers of finance, talent, and technology, small and medium-sized enterprises (SMEs) have faced uncertainty and risks if they fail to engage in digital transformation (DT). A good choice is for SMEs to choose solutions already on the market, provided by professional DT solutions suppliers. However, how to choose the most suitable DT solution remains a major challenge for SMEs. Thus, to help SMEs to select an appropriate DT solution, we proposed a novel, prospect theory-based evidential reasoning (ER) assessment method under a hesitant picture fuzzy linguistic sets (HPFLSs) environment. First, the novel distance measures of picture fuzzy sets (PFSs) and HPFLSs are proposed, then, based on the proposed distance measures of HPFLSs, novel, prospect theory formula are constructed. Additionally, a novel HPFLS ER method was developed to aggregate the evaluation information. Afterward, an assessment and selection decision approach for DT solutions for SMEs, based on the prospect theory-based ER of HPFLSs, was conducted. Finally, actual examples of DT solutions for SMEs to illustrate the decision-making approach were used to verify the effectiveness of the proposed method, and the conclusions were summarized.

With the surge in popularity of electric vehicles (EVs), managing retired batteries has emerged as a critical issue. Challenges such as information asymmetry and traceability difficulties amplify the risks and costs associated with recycling and reusing retired batteries. Inadequate collaboration within the supply and recycling chains diminishes recycling efficiency, leading to serious ecological pollution and resource wastage. This paper examines a tri-echelon closed-loop supply chain involving an EV manufacturer, a retailer, and a third-party recycler. By employing non-cooperative game theory, we model and compare the equilibrium outcomes of five cooperative strategies among these three players. We further scrutinize the influence of environmental policies, such as carbon trading or reward-penalty, and the degree of battery information traceability permitted by blockchain technology on the efficacy of these cooperative strategies. By using cooperative game theory, specifically the Shapley value, we suggest a fair profit distribution mechanism that promotes coordination within the supply chain. Moreover, we use empirical data from the Chinese EV market to conduct a case study. Our findings provide theoretical insights for firms and industries grappling with the escalating challenge of retired EV batteries.

考虑绿色度和创新指数,运用匹配博弈理论探讨绿色供应链成员匹配策略及匹配方案的均衡性,进一步对比集中和分散两种模式下,成员收益、原料绿色度、产品价格等因素对所得匹配方案的影响.对比发现,集中控制模式下制造商绿色创新投入、产品价格和整体收益略高,表明匹配博弈策略有助于优化链上成员合作,对间接提升绿色供应链整体收益具有促进作用.

This paper presents a systematic two-stage analysis to improve a three-echelon closed-loop supply chain cooperation and coordination under differentiated carbon tax regulation, which enriches non-cooperative and cooperative game theory applications in remanufacturing circular economies. Firstly, we utilize non-cooperative games to explore the optimal operations of new and remanufactured products across five coalitional models. Then, we adopt cooperative games to achieve profit coordination. Specifically, Shapley values are employed for allocating a grand coalition profit in a centralized setting. In order to alleviate carbon taxpayers' fairness concerns, we proposed a novel revised Shapley value algorithm incorporating differentiated carbon tax costs to improve their utility. The results show that regulators can reduce emissions by adjusting carbon tax rates on different products more effectively. The optimal environmental and economic performance can also be achieved by forming a grand coalition. Shapley value algorithms achieve the supply chain system Pareto improvement. Moreover, carbon taxpayers' (i.e., manufacturer and remanufacturer) utilities in our revised Shapley are improved by 20.15% and 77.71%, respectively, compared with the classical Shapley value under certain conditions. These results benefit regulators' carbon tax policy formulation and industrial managers’ remanufacturing practices.

为研究制造商风险规避行为对供应链运作及减排的影响,建立了一个两阶段模型.首先,构建由制造商与零售商组成的集中和分散决策博弈模型,求解并分析碳税税率以及制造商风险规避程度对均衡解的影响.进而,基于合作博弈理论,提出了一种修正Shapley值利润分配机制,通过合理分配利润来激励供应链成员协作共担风险.结果表明:在较低范围内提高碳税税率,可以促进集中决策下的减排水平和减排量,但分散决策不一定.集中决策具有明显的经济和减排双重优势,但制造商风险规避行为会削弱这种优势,并可能导致集中决策利润低于分散决策.受制造商风险规避影响,供应链利润分配存在严重的"背离"现象.所提的修正Shapley值在制造商低风险规避时可实现供应链协调,并且在利润分配的公平性上优于经典Shapley值.