Government attention is crucial to policy change and outcome. For local agents, their attention is explained by various factors, and a thorough understanding is needed to integrate diverse perspectives. Combining vertical, horizontal, local, and personal influences, we formulate a conditional embeddedness framework for local agents. The framework explains that agents' attention is primarily directed by principals, but this directed shift is horizontally and locally shaped by political and economic contexts, conditional on agents' traits. Empirically, we test attentional responses of 328 prefecture-level governments to an environmental reform in China. Results corroborate our hypotheses, showing that local government attention generally increases following a reform, but the increases vary due to promotion competition and firm dominance and are further moderated by mayors' education and work experience. The findings demonstrate the importance of interactions between contextual and individual features in the shifts of local government attention and their responsiveness in a principal-agent relationship.

Household laundry activities generate substantial environmental pressures, particularly through carbon emissions and water-related impacts. In China, pronounced heterogeneity in climate, resource endowment, and socio-technical conditions leads to spatially differentiated laundry practices and environmental outcomes. To systematically capture this complexity, this study develops an integrated analytical framework based on an adapted multi-level perspective (MLP) of socio-technical transition theory, linking landscape-level contextual conditions, regime-level practices, and technological configurations to guide variable selection, empirical modelling, and result interpretation. Using survey data from 2,728 urban residents across China, the study integrates statistical analyses (descriptive statistics, chi-square tests with Cramer’s V, and logistic regression), environmental footprint accounting (carbon, direct water scarcity, and direct eutrophication footprints), and uncertainty and robustness assessments (bootstrap resampling, one-factor-at-a-time sensitivity analysis, and structural robustness tests). Results reveal statistically significant regional heterogeneity in laundry practices and appliance characteristics after controlling for socio-demographic factors, with Cramer’s V values suggesting small to moderate effect sizes. Northeast China, North China, and South Central China emerge as dominant hotspots for carbon emissions (55.716 kg CO2 eq/person), direct water scarcity footprint (3.958 m3 H2O eq/person), and direct eutrophication potential (0.052 kg PO43− eq/person), respectively. Uncertainty and robustness analyses indicate that regional hotspot patterns are robust to sampling variability and parameter perturbations, while structural robustness tests based on regional sample exclusion do not affect the interpretation of spatial patterns, despite minor variations in absolute values. Beyond baseline estimation, scenario analysis demonstrates that environmental outcomes are shaped by interactions between household practices, appliance efficiency, and regional contextual conditions rather than by isolated behavioral or technological drivers. These scenarios are explicitly framed as exploratory analytical tools rather than predictive forecasts. By integrating socio-technical theory with empirical footprint modelling and uncertainty quantification, this study demonstrates how similar household behaviors can generate divergent environmental impacts across regions and provides a robust analytical basis for context-sensitive sustainability strategies.

Cities are key drivers of economic progress and play a decisive role in global climate action. Cities’ gross domestic product (GDP) data serves as a critical tool for evaluating economic progress and also offers a window into broader well-being, such as healthcare, education, and infrastructure. However, city-level GDP projections remain absent in China. This study uses the Cobb-Douglas production model to develop city-level GDP from 2020 to 2100, accounting for China’s unique socio-economic conditions. The dataset is validated by comparing its results with historical data and other future GDP scenarios. We develop 27 scenarios by varying technology, fertility, and intercity interaction across three levels each, considering China’s two-child/three-child policy, regional collaborative development, western development strategies, and technological advancements like AI. Among these, the Labor-Constraint Pathway and BAU Pathway closely align with the SSP1 and SSP2 scenarios, respectively. These scenarios provide a more accurate representation of future city-level GDP dynamics in China.

The intersection of artificial intelligence (AI) and industrial ecology (IE) is gaining significant attention due to AI's potential to enhance the sustainability of production and consumption systems. Understanding the current state of research in this field can highlight covered topics, identify trends, and reveal understudied topics warranting future research. However, few studies have systematically reviewed this intersection. In this study, we analyze 1068 publications within the IE–AI domain using trend factor analysis, word2vec modeling, and top2vec modeling. These methods uncover patterns of topic interconnections and evolutionary trends. Our results identify 71 trending terms within the selected publications, 69 of which, such as “deep learning,” have emerged in the past 8 years. The word2vec analysis shows that the application of various AI techniques is increasingly integrated into life cycle assessment and the circular economy. The top2vec analysis suggests that employing AI to predict and optimize indicators related to products, waste, processes, and their environmental impacts is an emerging trend. Lastly, we propose that fine-tuning large language models to better understand and process data specific to IE, along with deploying real-time data collection technologies such as sensors, computer vision, and robotics, could effectively address the challenges of data-driven decision-making in this domain.

The circular economy is vital for sustainability, yet its resilience to unexpected socio-economic shocks is not well understood. This study explores the impact of one of the major global disruptive events, the COVID-19 pandemic, on the circular economy by focusing on copper recycling. Using transaction-level data from a waste trading platform and causal inference methods, we evaluated how the pandemic disrupted copper scrap supply and transactions. The findings indicate significant and enduring negative effects, including reduced trading volumes, prices, and material diversity. The disruption was uneven across sectors: laborintensive industries were most seriously affected, while technology-intensive and capital-intensive sectors demonstrated greater resilience. To enhance recovery and strengthen the resilience of a circular economy, we recommend coordinating policy and market signals, incentivizing resilience-enhancing practices, and balancing efficiency, sustainability, and resilience goals. By mitigating adverse effects from unexpected disruptions, these strategies aim to foster a more resilient circular economy.

This study examines the socio-economic factors influencing material efficiency in over 100 countries from 1970 to 2016, amidst growing material consumption and environmental concerns. It assesses material efficiency using production and consumption metrics, revealing four distinct trajectories reflecting diverse economic and demographic contexts. The research shows that globally, economic growth and technological advancement significantly enhance material efficiency. However, the effects of such factors vary greatly by income, human development level, and aging demographics. As countries advance in income and human development, and as their populations age, the beneficial effect of economic growth on material efficiency diminishes. Conversely, the positive impact of technological progress intensifies with higher levels of human development. Moreover, as societies age, population growth shifts from hindering to promoting material efficiency, especially in "super-aged" societies. The findings highlight the need for context-specific strategies for material efficiency, acknowledging the unique stages of development and demographics of each country.

Monitoring technologies are widely used by upper-level government to enhance local compliance and achieve better environmental quality. Given their limited spatial accessibility of monitoring, how these technologies shape local enforcement strategies is uncertain. Here we show the impact that the nationwide establishment of air-quality-monitoring stations in Chinese cities had on local pollution reduction, enforcement and social welfare. Leveraging high-resolution datasets and a quasi-experimental design, we found that the newly introduced monitoring stations led to an 8.03

The remarkable economic growth of China over recent decades has been accompanied by significant achievements in poverty reduction. However, this growth has also led to increased resource consumption and greenhouse gas (GHG) emissions. It's now evident that addressing the triple challenges of climate change, biodiversity loss, and pollution hinges in part on China's capacity to curtail its resource use and emissions. Our study utilises an integrated economic modelling framework to assess China's role in the global context. We evaluate ambitious policies in resource efficiency, GHG reduction, and land-use transformation within China. Our findings indicate that, under such ambitious policies, China could likely achieve peak material use and emissions by 2030 and attain net zero emissions by 2050. Moreover, our analysis underscores that China can play a pivotal role in helping achieve global climate goals in collaboration with the rest of the world. Implementing well-designed policies, including the adoption of a circular economy approach, might enable China to meet these environmental targets with minimised economic costs.

Monitoring technologies are widely used to enhance compliance for better environmental quality. While these technologies often bring pollution reduction, much is unknown about the enforcement strategy of local authorities and its welfare implications, given technologies’ spatially limited accessibility. Here we show the impact of nation-wide establishment of 1,436 air-quality monitoring stations in Chinese cities on pollution reduction, local enforcement strategy, as well as social welfare consequences. Leveraging high-resolution datasets and a quasi-experimental design, we find that newly introduced monitoring stations led to an 8.03% (9.6972 µg/m 3 ) reduction in PM 2.5 concentrations in urban areas. Within those areas, the stations resulted in 0.57% (0.3046 µg/m 3 ) more reduction in PM 2.5 concentrations in areas accessible by the stations compared to non-accessible areas. The air-pollution reduction was associated with decline in industrial activities and change in land use, and led to higher housing price in technically accessible areas within cities. The back-of-the-envelope calculation shows substantial urban spatial inequalities in welfare consequences, driven by health benefits, for urban residents close to monitoring stations. Our findings suggest that the application of monitoring technologies should take environmental justice into consideration for a more comprehensive idea of sustainable development.

Regulating organizations to align their private interests with public interests is important, especially for collective action problems in climate and sustainability governance. Whereas these issues are not satisfactorily addressed by conventional regulations, norms are envisioned as a promising alternative. But norm-based policy instruments are not well understood regarding their scalable effects on substantive organizational actions, given the presence of other regulations. We advance a conceptual framework, accounting for norm-based interventions' potential effects on organizational actions and their differences from conventional regulations in institutionalized governance. Based on the setting of cleaner production (CP) in China and an event study strategy, we provide empirical evidence consistent with the framework: non-regulatory, norm-based interventions led to nation-wide, significant improvement in plant-level CP; the effects were stronger via network-based diffusion and local internalization, weakened by extrinsic motivation from regulations, and associated with managerial conformity, not innovation. We estimate substantive benefits of norms in public goods provision, with the amount of water saved equivalent to the consumption of a water-scarce province in China. Our findings provide consistent explanations for norm-based instruments in real governance settings, showing them as a complement to other policies in shaping organizations and guiding proactive transitions to address global challenges.

Water is crucial for achieving the UN Sustainable Development Goals, particularly SDG 6. As a major source of water use and pollution, industrial sector requires improved water management based on more systematic and refined analysis. Such analysis, however, is compromised by the accuracy, granularity, and coverage of industrial water data. Here we present an open dataset of China’s industrial water use, compiled from 1,480,265 plant-level reports. This high-resolution multi-scale dataset offers unparalleled details, supporting multi-scale analysis at the province, city, and county levels, and across 2-digit, 3-digit, and 4-digit industrial classifications. It provides comprehensive information on water use, recycling, pollution, and wastewater processing. Such data enables further macro- and micro-level analysis, including multi-regional input-output analysis, structural decomposition analysis, statistical analysis, machine learning, as well as many other advanced analytical methods. This dataset can equip researchers and policymakers with a valuable tool to advance sustainable water management, fostering alignment with global sustainability goals.

危机事件引致政府及时回应,进而推动制度改革与经济社会转型发展.但政府的危机回应能否以及如何在预期效果外产生有助于纾解危机背后政策元问题的其他政策效应,是政策设计与评估往往忽视的重要议题.围绕地方政府回应太湖蓝藻这一环境焦点事件的案例,结合对政府回应的定性梳理与准实验设计的量化评估,本文探索危机回应特征与企业创新这一非预期政策效应间的关系.研究结果显示,地方政府在迅速回应焦点事件以外持续且多样的政策回应特征,诱发了企业总体创新与绿色创新持续增长的非预期效应,从而纾解了危机背后环境与经济间长期矛盾的元问题.研究提出了政策元问题与非预期政策效应间的关系,识别了危机回应非预期效应的存在及触发条件,丰富了对危机回应政策效应及其实现机制的理解,并给出重要启示:危机后形成持续的回应机制可促进解决元问题的长期政策效果实现;充分理解政府持续回应的有效性及其机制,是推动政策问题根本解决的关键.

气候政策体系中的一个重要环节是加强面向公众的气候变化传播.在传播中使用并创新框架策略能达到影响公众认知与行为,提升传播效果的目的.但现有气候变化传播框架策略研究在媒介选择和方法运用上存在不足,难以支撑框架策略在形态丰富性和识别准确性上的创新.从视频媒介YouTube上提取234个气候变化纪录片的字幕构建了130万单词量的语料库,并基于无监督机器学习的主题模型网络分析法识别其中的框架策略.结果发现,气候变化视频传播语料中存在"环境威胁框架""人类威胁框架"和"危机可控框架"3种策略.前两者分别强调气候变化对环境和人类生存的威胁;后者则强调气候变化所造成负面后果可通过科学、技术及多主体共同行动来有效控制.这是已有文献中未曾发现的复杂框架策略.研究发现对推进中国气候变化传播实践具有重要启示.

Food waste is a global concern and is increasingly addressed by various policies and campaigns, especially in the consumption stage. Among these efforts, a promising instrument is gentle interventions based on nudges. To investigate whether and how a nudge works, we develop a theoretical framework and conduct a meta-analysis to synthesize empirical effects of nudges on reducing consumption food waste. The meta-analysis's summary effect size of cognitively-oriented nudges is a 0.27 SD (Cohen's d) reduction in food waste, and that of behaviorallyoriented ones is a 0.54 SD reduction. The effects of nudges are robust across sampled populations (i.e., U.S. vs. non-U.S. samples) but vary across settings (i.e., public vs. private). We further map nudge interventions to the driving factors of food waste behaviors and reveal potential research gaps in the literature. Based on these findings, we discuss implications for policy making to reduce food waste.

China's thermal power sector accounted for 45 % of total carbon emissions. Significant regional discrepancies exist between the plants to reach the carbon peaking target, which was overlooked in current research. This study investigates the regional discrepancies of the thermal power plants towards the carbon peaking target firstly. A thermal power plant database with similar to 4500 power units in 298 cities to evaluate the emission reduction potential, expected carbon peaking period, and co-benefits. Results show that the carbon emission of the sector will experience a 7.2 % rise between 2019 and 2035, but the emission intensity decline by 27.6 g CO2/kWh. Nearly-one-third of the cities have risks not reaching the carbon peaking target before 2030, while most cities can reduce air pollutant emission. This study reveals the inconsistency between spatial carbon emissions and reduction potential of the thermal power plants and proposes several policy suggestions to the whole sector's decarbonization.

The linkages between CO2 and air pollutant mitigations in the steel industry, including synergies and trade-offs, have significant spatial heterogeneities. Previous research has only investigated the linkages at the industrial level and overlooked the spatial heterogeneity features, which hinders the precise decision making on mitigation strategies. This study quantifies the linkages between CO2, SO2, NOx, and PM mitigations across cities in China's steel industry. A database of 3689 steel production units is established to calculate the emissions. Then, the linkages of mitigation targets of each city are quantified by setting the tailored mitigation pathways in 2019–2035. The results show that the emissions are highly spatially heterogeneous, as the top five contributing cities account for 27.7–33.2% of the total emissions of China's steel industry, but their emission efficiencies are not necessarily worse than the average. The mitigation pathways cause two-sided relationships, as 138 cities will earn mitigation co-benefits to varying degrees, but other 11 cities show the trade-offs. In addition, the mitigation pathway will lead to the highest economic costs as 51.2 billion CNY/a, bringing heavy economic burdens to some cities. This study enriches the co-mitigation management theory and supports the formulation of spatially differentiated mitigation targets and measures.

Clean, low-carbon energy transition has been a global trend in pursuing climate mitigation and sustainable development, with residential heating being an essential component. Despite its substantial climate, environmental, and health benefits, the social impacts of residential energy transition are insufficiently understood. Based on a difference-in-differences design, we identify the causal effects of a large-scale clean heating policy on public perceptions of their social status in northern China. We find substantial improvement in individuals’ social status immediately following the heating renovation, which is robust over a set of empirical specifications but diminishes in the long term. The transition benefited not only those directly experiencing renovation but also others in the same areas. The improved social status was driven by perception of higher income and bettered health condition. The findings indicate a sustainable and inclusive transition of clean heating, and call for additional measures to maximize its social benefits.

Ancillary impacts of climate policies on issues other than climate consequences are important for the cost-benefit analysis of optimal policy design, policy-making process, and climate communications. A common perception, relying on scenarios and simulations, suggests substantial co-benefits of air quality and human health im-provements from climate mitigation measures. Based on a quasi-experimental design for causal inference, however, we show at the firm level the existence of adverse side-effects of a regional carbon emissions trading program in China on local air pollution. An average firm in the emissions trading program emitted more local air pollutants compared to its counterpart outside of the program. The adverse side-effects were particularly sig-nificant in the power sector, where pollution control was more progressive. After ruling out possibilities of data manipulation or intended coordination in policy implementation, we reveal that conflict in firms' dual compliance to climate and environmental policies may explain the unintended consequence: when facing a price of carbon from emissions trading, firms have lower incentives in pollution control, which is energy intensive. Our findings suggest that the direction of spillovers from climate policies are context-specific, depending on the stringency and instrument choice of existing environmental policies. Improved policy enforcement and infor-mation provision of mitigation measures can help avoid unintended policy impacts.

Emissions trading systems (ETS) have been a widely-adopted policy instrument for global climate mitigation and a key choice in China's pledge for peaking emissions and carbon neutrality. Broader adoption and linkage of ETS programmes require a better understanding of whether, to what extent, and how existing regional programmes address carbon emissions at an aggregate level. Combining a synthetic control method and event studies, we adopt a comprehensive evaluation framework to investigate regional mitigation effects, pathways, and ancillary impacts in three Chinese regions with four independent pilot ETS programmes. The findings show economy-wide responses to pilot announcement even in nonETS sectors, but enduring mitigation only within ETS sectors. Mitigation was achieved via improvement in energy efficiency and fuel switch, without impairing industrial activities. There were local air-pollution reduction co-benefits but no leakage or spillover. Bounded extrapolation from the pilots suggests 18%-20% reductions can be achieved in non-pilot regions by a national market, which could learn from pilots' experiences to broaden sector coverage and ensure policy consistency and transparency. Regional ETS were able to stabilize emissions with little cost, providing rationale for rapidly developing economies to adopt such systems.