We study whether place-based tourism policy can promote corporate green innovation in a developing economy. Exploiting the staggered rollout of China's 5A National Tourist Attraction designation across 240 prefectures during 2003 to 2024, we find that designation increases green patent applications by 19%, concentrated in substantive invention patents. The result survives heterogeneity-robust estimators, an instrumental variable strategy using historical cultural heritage sites, propensity score matching, placebo tests, and extensive sensitivity checks. Mediation analysis reveals that public environmental attention, rather than direct regulatory coercion, serves as the dominant transmission channel. The treatment effect concentrates among tourism-linked, high-absorptive-capacity, and non-polluting firms, consistent with opportunity-driven voluntary adoption. A Spatial Durbin Model documents significant positive spillovers to neighboring cities, implying that conventional within-jurisdiction evaluations underestimate aggregate innovation gains by approximately one-fifth. These findings demonstrate that tourism governance can generate environmental innovation externalities extending well beyond the tourism sector.

We provide the quantitative evaluation of the incentive structure of China National Emission Trading Scheme (CN ETS) by analyzing 2,282 compliance firms and find that CN ETS provides economic incentives for emission abatement through trading profits but exhibits a Matthew effect, whereby firms with larger emission reductions achieve higher marginal profits. In the second compliance cycle, the market incentive effect improved, evidenced by an increase in trading profits per ton of emission reductions and a weaking of the Matthew effect. The benchmark allowance allocation has effectively encouraged low-emission-intensity coal-fired units while expediting the phase-out of high-emission intensity units. However, gas-fired units, despite their lowest emission intensity and high flexibility, receive weak incentives. Central state-owned enterprises and units with prior experience in China’s ETS pilots, exhibit lower trading participation. Enhancing allowances scarcity, implementing paid allowances, strengthening compliance enforcement and penalties, and increasing trading activity are suggested to improve the CN ETS.

Human-wildlife relations in alpine ecosystems reflect the combined effects of climate change, land-use transitions, and governance capacity. Rather than focusing solely on local conflict dynamics, this study investigates how adaptive governance can be institutionally embedded to anticipate and respond to ecological and social uncertainties. Drawing on multiple data sources-including official and historical records of eco-ranger co-management policy, village-level focus group discussions. We also conducted a three-round Delphi process with 30 multidisciplinary experts (conservation scientists, rangeland managers, and policy practitioners)-we developed collaboratively a governance framework linking climate risk assessment to decision-making processes in Sanjiangyuan National Park (SNP). The evidence from multiple sources identified four core adaptation domains: (i) climate-informed spatial planning, (ii) flexible grazing regulation, (iii) participatory monitoring of wildlife mobility, and (iv) cross-sector coordination mechanisms. Expert consensus emphasized the need for adaptive feedback systems to integrate real-time ecological data (including opportunities to link with quantitative ecological indicators) into local decision cycles. By synthesizing insights across multiple perspectives, this study proposes a governance framework for operationalizing adaptive governance in climate-sensitive protected areas. Unlike previous work focusing on community-level perceptions or household adaptation strategies, this paper advances a participatory governance approach centered on institutional design and social learning. It contributes to broader debates on how conservation governance can evolve toward resilience-oriented, co-managed systems under environmental change. The proposed framework provides concrete steps for integrating climate information into routine protected area management. Future work should validate the framework with more representative data and a more diverse expert pool and cross-regional application.

China's national emissions trading scheme (CN ETS) is the world's largest carbon market in terms of covered emissions, yet rigorous empirical evidence on its mitigation effectiveness remains limited. Using a balanced panel of 1,957 thermal power units from 2018 to 2024, this study estimates the causal impacts of compliance pressure under the CN ETS on CO2 emissions. Units with allowance deficits reduced CO2 emission intensity by 0.8% and total emissions by 3.5%. Emission reductions are concentrated among small coal-fired units and are driven by efficiency improvements, higher heat supply ratios, and improved fuel quality. In contrast, the impacts on large coal-fired units are limited. Greater intensity reductions are observed among local state-owned and private firms, captive plants, non-pilot units, and technologically less advanced units. Overall, the intensity-based design provides limited incentives to curb output among low-emission-intensity units, suggesting the intensity-based mechanism functions as a transitional arrangement toward a cap-and-trade system.

In recent times, the global environmental repercussions have intensified the imminent threat of global warming and climate change. In response, implementing innovative approaches and sustainable practices for ecological preservation remains a considerable challenge even for developed nations, such as G7. It is therefore inevitable to identify key factors driving the progress of environmental sustainability. Motivated by this, the current research is an earliest attempt which delve the impact of artificial intelligence (AI), eco-innovation efficiency (EIE), environmental policy stringency (EPS), and green growth (GG) on load capacity factor (LCF) under the load capacity curve (LCC) framework to achieve environmental sustainability in G7 countries. In this regard, innovative approaches of Driscoll-Kraay standard errors (DKSE) and panel-corrected standard errors (PCSE) are employed to investigate the long-run relationships, using the data from 1990 to 2020. The findings highlight that: (i) eco-innovation efficiency primarily promotes environmental sustainability by improving load capacity factor, which is advantageous for G7 countries; (ii) artificial intelligence, environmental policy stringency, and green growth inhabits environmental sustainability by decreasing load capacity factor, which are detrimental for G7 countries; (iii) the LCC hypothesis is invalid in G7 countries illustrating an inverted "U-shaped" relationship between income and LCF. This implies that economic growth initially improves environmental sustainability but later deteriorates the environment after reaching a certain threshold. These findings emphasize that decisionmakers should restructure energy and environmental policies for G7 countries by prioritizing AI technologies, augmenting stringent environmental policies, implementing clean energy initiatives, and decoupling economic growth and resource consumption along with further strengthening ecologically efficient technologies.

Green ammonia, a stable hydrogen carrier, is regarded as a potential pathway for decarbonizing hard-to-abate sectors and addressing the seasonal variability of renewable energy, playing an important role in the global pursuit of carbon neutrality. However, its deployability and production costs depend strongly on the spatial distribution of renewable resources and are significantly constrained by land use. Focusing on marginal land in China, namely land with relatively low current utilization intensity after excluding major agricultural, urban, ecological, and topographic constraints, this study estimates green ammonia production potential and costs at an approximately 10 km spatial resolution. This study first identifies suitable areas under land-use, topographic, and ecological boundaries. An islanded production system integrating generation, storage, electrolysis, and ammonia synthesis is constructed to derive grid-level production potential and the levelized cost of ammonia. To characterize uncertainty, ranges of potential and cost are reported based on the upper and lower bounds of key techno-economic parameters. Results show spatial clusters of green-ammonia potential jointly shaped by natural resources, land-use constraints, and system configuration; the levelized cost of ammonia declines with technological progress and production scale-up. Under land-use trade-offs on marginal land, green ammonia and energy crops display regionally contrasting cost advantages. This study uses the technically developable potential of green ammonia to represent the maximum production potential achievable within a region under physical, environmental, and selected policy constraints. This indicator is intended to explore the spatial heterogeneity of green-ammonia production and its underlying mechanisms, thereby providing scientific support for region-specific investment decisions and land-use policymaking.

Green technology innovation efficiency (GTIE) serves as a critical metric for evaluating the capacity of innovation systems which simultaneously delivers economic, technological, and environmental benefits. Despite growing recognition of the roles played by energy security (ES), ecological innovation (EIN), and renewable energy transition (RET), their integrated impact on GTIE remains underexplored particularly within Middle East and North Africa (MENA). This study fills this gap by examining the joint influence of ES, EIN, and RET on GTIE across 14 MENA countries during 2000–2023. A comprehensive multi-dimensional analytical framework comprising the novel super-efficiency ray slack-based measure (SR-SBM) model and the method of moments quantile regression (MMQR) approach is applied. The SR-SBM findings reveal that GTIE in the region is relatively low, with most inefficient countries requiring at least 61.5% improvement to reach the efficiency frontier. Considerable disparities are observed, with Israel exhibiting the highest efficiency and Iraq the lowest. The MMQR result indicates that while ES negatively influences GTIE in less efficient countries, EIN and RET exert a consistently positive effect across the distribution. These insights underscore the importance of prioritizing ecological innovation and accelerating renewable energy transition, even in energy-secure context, as essential strategies for enhancing GTIE in MENA region.

Understanding the associations between climate change and agricultural productivity is crucial for developing strategies to safeguard food security. This study examines the long-term relationships between temperature and precipitation variability and the total factor productivity (TFP) of wheat, rice, soybean, and maize in China, using provincial data from 1978 to 2018. We employ a three-stage analytical approach: first, constructing productivity frontier via Data Envelopment Analysis (DEA); second, decomposing TFP growth into technological advancement, weather-related frontier shifts, input adjustments, and efficiency changes; and third, utilizing panel regressions to identify nonlinear, crop-specific associations with climate variables. Our decomposition findings indicate that climate change is negatively associated with TFP growth, accounting for declines of 4.8

China is the world's largest source of methane (CH4) emissions and has signalled its intention to incorporate methane into its climate commitments. Designing effective methane-mitigation strategies requires robust estimates of up-to-date emissions, particularly when the pronounced spatial heterogeneity and temporal variability of each emission source are considered. However, there is currently an absence of source-level, up-to-date and dynamic CH4 emission estimations in China, constraining the formulation of measurable targets. In this study, we present the Chinese Methane Emissions Database (CMED), a nationwide, source-level, monthly emission inventory covering the period 2018-2024. The CMED provides a comprehensive account of anthropogenic CH₄ emissions in China. Uncertainty analysis of the CMED indicates that CH₄ emission estimates fall within an acceptable range (±3.57%), underscoring the robustness of the dataset. This comprehensive dataset enables more accurate analyses by providing integrated, source-level and temporally explicit information, offering critical support for policy evaluation under China's new round of Nationally Determined Contributions climate commitments released in 2025.

The pursuit of carbon neutrality in China demands a rapid, spatially informed scale-up of renewable energy, including biomass, yet high-resolution, policy-aware data for site-specific planning remain scarce. To bridge this gap, we develop China's high-resolution spatially explicit biomass resource potential dataset, which integrates five biomass categories (agricultural residues, forestry residues, energy crops, animal manure, and municipal waste) at 1 km resolution for 2020, with projections to 2050. This dataset incorporates key constraints such as food security, ecological conservation, and land use suitability. It provides heat value potential distribution maps in GeoTIFF and PDF formats, and heat value potential data in Excel format. By combining multi-source geospatial data, statistical downscaling, and machine learning, this dataset enables precise assessment of resource conditions and provides forward-looking planning for biomass power deployment, rural revitalization, and carbon reduction strategies, thereby meeting China's critical need for integrated, location-aware open data in energy and land-use decision-making.

Promoting the synergy of pollution reduction and carbon mitigation is a crucial strategy for China’s current climate and environmental governance. This requires active policy measures from both the production and consumption sides. This study constructs a tripartite evolutionary game model of the government, enterprises, and consumers, while considering the effects of green transformation on pollution reduction and carbon mitigation. It explores the impact of the coordinated implementation of command-control, market-incentive, and voluntary policies on promoting green production and consumption. The key findings are as follows: 1) The decisions of the government, enterprises, and consumers are interrelated. To drive the system toward the optimal equilibrium state of green production and consumption, breaking the initial path dependence is crucial. The government must enhance regulatory intensity in the early stages and strengthen publicity and education for enterprises and the public, thereby effectively elevating the initial participation willingness of all three parties. 2) Policy implementation should be tailored to industrial heterogeneity. For industries with substantial abatement potential and high emission intensity, market policies are most effective; these sectors should be prioritized for inclusion in the carbon trading market with differentiated quota schemes. Conversely, for industries with lower emission bases and limited abatement potential, low-cost administrative controls or direct environmental taxes are more suitable. 3) The substitution relationship among policies requires dynamic alignment with market and technological maturity, yet a diversified policy mix remains indispensable. When abatement efficiency and carbon prices are sufficiently high, market-based tools can lower compliance costs through streamlined processes, such as replacing environmental taxes with a single carbon trading mechanism. However, voluntary interventions on the consumption side cannot be substituted by market regulation or mandatory supervision.

Synergistic control of greenhouse gases and air pollutants in China is challenging due to spatiotemporal variations in weather and emissions that drive highly non-linear atmospheric chemistry. To provide precise, region-specific policy guidance for pollution and carbon reduction, this study integrates high-resolution PM2.(5) and O-3 datasets, classifying China at a 1-km grid scale into four pollution evolution types based on synergistic PM2.(5)-O-3 improvements. We employed stacked ensemble machine learning to model interactions between anthropogenic emissions and pollution evolution under real meteorological conditions. Shapley Additive Explanations (SHAP) was used to identify the top three anthropogenic emission factors that need to be prioritized for control in each regional pollution evolution type to achieve the coordinated improvement of PM2.5 and O-3 pollution. Key findings reveal that from 2015 to 2022, the effectiveness of controlling traditional pollutants (PM10, NMVOC) diminished. CH4, SO2, NOx, and N2O emerged as increasingly critical targets, with priority shifting toward CH4 and N2O. While some regions effectively reduced PM2.(5), persistent O-3 pollution increased. Scenario simulations under uniform 2021-2022 meteorology show that a blanket 5 % cut across all species enlarges the O-3-rebound area by > 15 % relative to the baseline. Prioritizing CH4, N2O, and SO2 control in specific regions can expands the co-improvement area by similar to 40 %., while other areas still require comprehensive control of multiple factors for synergistic air quality and climate benefits. This methodology enables tailored, region-specific emission control strategies adaptable to spatiotemporal variations in human activities and natural conditions.

Climate change is a critical global challenge, intensifying risks and influencing corporate financial behavior. Employing a high-dimensional fixed-effects model on a panel dataset of 11,167 firms across 59 countries, the study finds that climate change acts as a significant catalyst for corporate financialization, compelling firms to increase their allocation of capital to financial assets. This effect, robust under various checks, operates via two mechanisms: climate change exacerbates financing constraints, prompting firms to accumulate liquid assets for precautionary motives, and it dampens the efficiency of real investments, driving a strategic reallocation of capital toward financial assets as a form of investment substitution. Heterogeneity is evident in relation to energy vulnerability, digitalization, industrial structure, carbon emission intensity, and Islamic countries. Furthermore, increased financialization leads to elevated dividend payouts. Adopting a global perspective, this study fills a critical gap, providing empirical evidence on firms’ strategic financial adaptation to climate change. It contributes to academic discourse and offers practical implications for policymakers and corporates.

Zero-carbon industrial parks are core demonstration carriers for global industrial deep decarbonization and a key research hotspot in climate change and sustainable development. However, three structural issues have long restricted the formation of a globally comparable research paradigm: fragmented accounting benchmarks, imbalanced research priorities, and poor generalizability of findings. This comment systematically analyzes the above deviations: divergent accounting rules across mainstream frameworks weaken cross-study comparability; studies overfocus on energy system transition while neglecting core process-level decarbonization; case-specific conclusions cannot adapt to the transition needs of developing economies. To correct these deviations, this comment proposes a universal minimum consensus benchmark, advocates a rebalanced research agenda, and outlines a differentiated globally adaptable framework with seven priority propositions, to advance the systematic development of the field.

Understanding the true cost effectiveness of emissions trading schemes (ETSs) is essential for advancing climate policy. In this study, we contribute by introducing the trade effect—the mechanism through which ETSs promote convergence in marginal abatement costs (MACs)—and by developing a novel empirical framework to quantify this trade effect using firm-level data. Our empirical findings suggest that conventional assessments, which focus primarily on emissions reduction (the cap effect), may overstate the effectiveness of carbon markets by neglecting cost efficiency. Unlike earlier studies, our results reveal that China’s ETS, in its current form, has not consistently performed better than administrative measures in terms of cost effectiveness, indicating an insignificant trade effect. This divergence highlights a potential gap in current ETS evaluations and underscores the need for critical adjustments in China’s ETS, including stricter emissions caps, fewer free allowances, and a long-term strategic roadmap. Our approach provides a new lens for ETS performance evaluation, offering policymakers actionable insights to refine carbon market designs globally.

Reaching the UN's sustainable development goals (SDGs) is influenced by a country's position in global value chains and its involvement in international trade. Here, we assess how changes in global trade patterns (CGTP) during 2004 and 2014 impacted 13 SDG indicators in 141 countries/regions which are further divided into four income groups. Trade pattern is characterized by the direction, composition, and magnitude of trade, indicating an economy imports what types (composition) and magnitudes of goods or services from where (direction). We find that CGTP aggravated socioeconomic and environmental inequality between countries in two ways: 1) the amount of indicators that significantly worsened due to CGTP decreased from 8 indicators (2004-2007) to 1 (2011-2014) for high-income and upper-middle-income countries, but increased from 5 to 14 for lower-middleincome and low-income countries; 2) CGTP led to a coupling of value added with most natural resource consumption and environmental pollution indicators for low-income countries, while they strengthened decoupling or reducing coupling for other countries. The findings imply one key to achieving SDGs is to address the inequality between rich and poor countries through implementing policy interventions that influence import and vertical supply chain thereby shifting the trade patterns towards environmental-economic decoupling in poor countries.

Supply chain decarbonization has emerged as a pivotal developmental trend in the era of achieving carbon neutrality. The advancement of green finance is critical for enhancing a low-carbon supply chain finance system, enabling businesses to align better with green transformation commitments. Although previous research has examined the impact of green finance policies on enterprises, the specific mechanisms through which green finance innovations influence green supply chains have not been adequately explored. We use data from China's listed companies (2009-2022) and employ the Difference-in-Differences model to assess the effects of green finance innovation and pilot reform policies on corporate supply chain carbon emissions and their underlying mechanisms. We find that green finance policies significantly reduce supply chain carbon emissions because these policies foster low-carbon supply chains by enhancing enterprises' green innovation capabilities, alleviating financing constraints, and elevating corporate executives' green awareness. We additionally identify that the carbon emission reduction effect of green finance innovation on suppliers is enhanced in enterprises with a robust high-tech foundation, limited supply chain finance, or those located in regions with stringent environmental regulations. We offer theoretical and empirical support for governments to develop and refine differentiated green finance policies aimed at achieving sustainable economic development goals.

SYNOPSIS This study examines the relation between asset-backed securitizations and future stock price crash risk in nonfinancial firms. We argue that the gain-on-sale accounting treatment for off-balance-sheet securitizations facilitates managers’ withholding of bad earnings news, leading to higher crash risk. Using a propensity score-matched sample of U.S. nonfinancial firms, we find that firms engaging in off-balance-sheet securitizations are associated with higher crash risk, especially for firms with gain on sales from securitizations. In 2010, the Financial Accounting Standards Board implemented SFAS 166/167 to tighten the criteria for securitization transactions to receive off-balance-sheet treatment. However, our difference-in-differences analysis shows no significant effect of SFAS 166/167 on reducing securitizing firms’ crash risk. Further analyses reveal that firms engaging in off-balance-sheet securitization before SFAS 166/167 conduct more real activity-based earnings management after SFAS 166/167. This evidence suggests that firms could continue to hide bad news through alternative channels as substitutes. Data availability: Data are available from the sources described in the paper.

As the world's leading paper producer and consumer, China's pulp and paper production sector poised to significantly contribute to global carbon emissions reduction in this sector. To assess the economic feasibility of CO2 reduction measures, this study estimated the least CO2 emission marginal abatement costs for 217 pulp production enterprises and 3817 paper production enterprises in China from 2008 to 2015. The estimation used a developed parametric directional distance function, considering various production and emission reduction strategies aligned with China's emission intensity reduction target. The estimation of marginal abatement costs incorporates emission abatement levels ranging from 1 to 100 % to facilitate the creation of marginal abatement cost curves. This study applied four specific emission reduction measures (i.e., expanding intermediate inputs, downsizing production, augmenting labor inputs, and enhancing capital investment) to identify the lowest marginal abatement cost. Additionally, we further explore the emission patterns among different enterprise sizes. Key findings include: i) The average annual marginal abatement costs for China's pulp and paper production sectors are 2059 CNY/tCO2 and 3908 CNY/tCO2 in this study during 2008-2015, respectively. ii) When the emission abatement levels of the pulp and paper production sectors exceed 35 % and 50 % respectively, the escalation in the CO2 emission marginal abatement costs becomes more pronounced. iii) Over 80 % of pulp and paper production enterprises opt for expanding intermediate inputs to achieve the least marginal abatement costs. iv) Compared to medium enterprises and small and micro enterprises, large pulp and paper production enterprises exhibit lower annual average marginal abatement costs, at 1162 CNY/tCO2 and 1840 CNY/tCO2 respectively, which are primarily located in the high-emission and low-abatement-cost pattern.

During the e-commerce era, manufacturers face strategic choices regarding the establishment of online (direct) channels alongside existing offline (retail) channels, particularly when selling inspection goods. Given that these decisions are significantly shaped by consumer channel preferences, this study incorporates the phenomenon of free-riding into the classic consumer choice model to examine how it influences the manufacturer's channel strategy by altering consumer preference for the direct channel. Our analysis yields several key insights. First, free-riding consistently intensifies price competition between the two channels, exacerbates double marginalization, and enhances consumer welfare. When pricing decisions are structured to allow both sophisticated and naive consumers to purchase from both channels, free-riding encourages the adoption of a dual-channel strategy at low levels but discourages it at high levels. However, when pricing decisions restrict specific consumer types to specific channels, free-riding always incentivizes the introduction of a direct channel. Third, our core findings remain robust in the presence of consumer heterogeneity-whether in their preference for direct channels or their degrees of free-riding. We further extend our analysis by relaxing key assumptions, which leads to new insights into strategic channel design. Our study contributes to a deeper understanding of the implications of free-riding in dual-channel retailing.