Search Results (1,317)

Risk perception has long been treated as a key driver of public climate attitudes and climate-friendly behaviors. Although prior studies have reported relatively modest levels of perceived personal climate threat among the Chinese public, they have also found strong recognition of anthropogenic climate change and solid support for climate policies. However, it remains unclear whether the relatively modest perceived threat to personal climate among Chinese respondents is associated with a lower willingness to engage in climate-friendly behaviors. To address this question, this study extended the multi-level risk and benefit framework to investigate how personal, societal, and national risk and benefit perceptions, and their associations with climate attitudes and behavioral willingness, were perceived via a national survey of Chinese respondents (N = 1500). Empirical analyses, however, show that personal and societal appraisals are not clearly distinguishable, whereas national-level appraisals form a distinct dimension. Regression results further indicate a systematic divergence in predictive patterns across appraisal domains. National-level appraisals are more strongly associated with climate attitudes, whereas proximate appraisals, particularly perceived personal benefits, are more closely related to behavioral willingness. While explaining the apparent paradox of relatively low perceived personal climate risk but comparatively strong climate attitudes in China, these findings extend research on the attitude–behavior gap by suggesting that national-level and proximate appraisals may play different roles in relation to climate-related attitudes and behavioral intentions in contexts characterized by strong state involvement in climate governance. Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

This article presents the results of a study assessing changes in climatic resources in various natural zones of the Zhetysu region, Republic of Kazakhstan, conducted based on long-term climate data for the period 1966 to 2024 (from 12 meteorological stations). The study examines current trends in climatic indicators in spatial and temporal aspects that influence agricultural land use within the region. The first part of this study examines current trends in climate indicators from both spatial and temporal perspectives within the Zhetysu Region of the Republic of Kazakhstan; the second part focuses on studying trends in climate indicators using the non-parametric Mann–Kendall test and the Sen’s slope test, as well as Fisher’s t-test. The authors identified divergent trends in relative air humidity and precipitation and detected a steady trend toward an increase in the average annual air temperature across the region. Based on the analysis of time series of climate-forming and climate–environment-forming indicators, a persistent increasing trend in mean annual air temperature was identified, while relative humidity, precipitation, and evaporation exhibited divergent (both positive and negative) trends across the territory of the region. The developed climate–resource-forming models and a series of estimated applied maps of climate indicators for 1966–1975 and 2016–2024 serve as the scientific basis for climate change forecasting and can be used by administrative bodies to improve agricultural land use strategies in the region. Full article

Climate change and low-carbon transition policies affect sustainable development by changing firms’ financing costs and investors’ capital allocation. This paper investigates whether and how climate-related information is priced in China’s equity market, focusing on firm-level carbon intensity and exposure to climate policy uncertainty (CPU). First, univariate-sorted portfolio tests confirm the existence of a carbon premium, as firms with high carbon intensity earn significantly higher average returns. However, the unconditional relation between CPU exposure and stock returns is insignificant. Bivariate-sorted portfolios reveal a strong interaction between the carbon premium and the CPU premium. The carbon premium is higher for firms with high exposure to CPU, whereas a significant and negative CPU premium appears among low-carbon firms and, in sector-level tests, is concentrated in non-energy firms. Further analysis demonstrates clear differences between energy and non-energy sectors, which may be attributable to cash flow risks and uncertainty in growth options. The findings contribute to climate-related asset pricing and sustainable finance research by showing that transition-risk pricing depends on the interaction between carbon exposure and policy uncertainty. Full article

How to promote regional agricultural sustainability through ecological compensation policy incentives and penalties has become a major concern worldwide. To evaluate the impact of ecological compensation reform on sustainable agricultural development, this study exploits China’s Ecological Compensation Incentive and Penalty Policy (ECIPP) reform as a quasi-natural experiment. Using panel data from 30 provinces (autonomous regions and municipalities) in China from 2012 to 2022, we construct a Staggered-Adoption Difference-in-Differences (SA-DID) model to identify the effects of policy implementation on the efficiency of agricultural ecological product value realization and its underlying mechanisms. The results show that: (1) the implementation of the ECIPP significantly improves the efficiency of agricultural ecological product value realization. On average, the policy increases the AEPVR efficiency score by 0.0869 units. (2) Mechanism analysis indicates that ecological compensation reform generates information transmission and structural adjustment effects. Specifically, the policy enhances government environmental attention and promotes the integration of agricultural industries, thereby improving the value conversion efficiency of agricultural ecological products. (3) Heterogeneity analysis reveals that the policy effect is more pronounced in regions with higher levels of public environmental concern and lower levels of fiscal decentralization. Furthermore, compared with the combined year-on-year and ranking-based assessment mechanism, the year-on-year assessment mechanism alone is more effective in enhancing policy performance. This study provides valuable insights for both developing and developed countries seeking to improve the effectiveness of ecological compensation policies and enhance the realization of value from agricultural ecological products. Full article

Sustainable urban air quality in tropical cities is threatened by interactions between climate change, extreme drought, and long-range wildfire smoke transport. This study investigated the causes of PM_2.5 pollution in Manaus, Brazil, under El Niño conditions during the 2023 drought, focusing on long-range wildfire smoke transport. The links among hydroclimatic drying, wildfire activity, and urban air quality were examined using hourly PM_2.5 observations, meteorological data, long-term climate records, MODIS hotspot and fire radiative power (FRP) data, and air-mass trajectory analyses. Significant long-term warming, decreasing precipitation, and a declining standardized precipitation evapotranspiration index were observed around Manaus during 1981–2024, indicating persistent drying. In 2023, severe drought and increased wildfire activity caused an annual mean PM_2.5 concentration of 15.09 µg m⁻³. Directional analyses, upwind FRP, potential source contribution function, and backward trajectories consistently highlighted the eastern and southeastern source regions approximately 500–2200 km from Manaus. These results indicated that PM_2.5 levels were more sensitive to spatial alignment between upwind fires and prevailing winds than to total fire activity alone. In conclusion, the 2023 PM_2.5 surge was driven by long-range wildfire smoke transport under intensified drying and drought, with implications for urban sustainability, public health, and climate-resilient early warning systems. Full article

Xinjiang experiences frequent dust storms, posing significant challenges to regional ecological security and public health. Based on the China High-resolution and High-quality Near-surface Air Pollutants (CHAP) dataset and ground monitoring data, this paper adopts the Potential Source Contribution Function (PSCF) to analyze the spatiotemporal characteristics of atmospheric particulate matter across Xinjiang and typical cities and to identify potential source regions and contribution intensities. The results show that (1) PM_2.5 and PM₁₀ concentrations are elevated in southern Xinjiang but reduced in the north, and particulate pollution in most areas has generally decreased. (2) Northern Xinjiang cities have high PM_2.5 in winter, while southern Xinjiang cities keep persistently high PM₁₀ levels. (3) The PM_2.5/PM₁₀ ratio is above 0.35 in northern cities, where pollution is dominated by fine particles affected mainly by human activities; southern Xinjiang is dominated by coarse particles from natural sources. (4) Particulate matter in Urumqi mainly comes from the northern Tianshan Mountains, with winter WPSCF over 0.9. Pollutants in Kashgar originate from both long-distance cross-border dust transmission and local emissions. These findings are of great significance for the sustainable development of Xinjiang and urban agglomerations along the Belt and Road. Full article

Urban air-quality monitoring networks are often sparse, leaving coverage gaps where particulate matter (PM) concentrations cannot be directly observed. This paper extends the CityAirQ pollution tracking platform and its mobile air-quality device prototype by introducing an AI-based benchmark for two Bucharest station networks across three deployment-oriented tasks: multi-station temporal forecasting (Task A), leave-one-station-out same-day spatial estimation (Task B), and a preliminary mobile-site prediction pilot at an uncalibrated location (Task C). The benchmark compares machine-learning models, including ensemble tree methods, recurrent neural networks, and lightweight graph-inspired architectures, evaluated under a unified time-aware rolling protocol. In Task A, the proposed Advanced Stage 0–3 pipeline achieves the best overall MAE (7.12 $\mathsf{\mu }$g/m³), a 4.7% reduction relative to Random Forest (7.47 $\mathsf{\mu }$g/m³), while the Seasonal naïve (10.41 $\mathsf{\mu }$g/m³), Persistence (11.51 $\mathsf{\mu }$g/m³), neural, and graph-inspired references perform worse under recursive forecasting. In Task B, the neighbour-only Random Forest reaches a mean $R^2$ of 0.873 on the classic four-station network and a median $R^2$ of 0.734 on the ten-station city-scale extension. Task C is reported as an exploratory six-day prediction pilot, not as deployment-grade validation: no co-located EPA FRM/FEM or equivalent reference monitor was available at the mobile location ℓ. The historical-transfer Random Forest retained a sample-limited positive PM_2.5 association with the raw mobile readings ($r=0.432$, $n=6$), while a strict one-day-ahead online persistence predictor reduced PM_2.5 MAE from 40.58 to 20.00 $\mathsf{\mu }$g/m³ on the five forecastable mobile days. Ultimately, accurate PM monitoring empowers sustainable urban planning, helping to mitigate exposure risks and supporting long-term public health and environmental sustainability initiatives. Full article

In summer 2022, Sichuan suffered an unprecedented compound heatwave-drought, cut-ting hydropower output and forcing a rapid coal-fired power ramp-up to secure supply, driving elevated emission intensities in its power sector. However, the fluctuations in power generation from thermal power and hydropower are significantly influenced by policy and economic factors. In meteorological-electrical coupling research, it is necessary to isolate the disturbances caused by major non-meteorological factors such as policy and economics on power generation to identify the true role of meteorological conditions. Therefore, this study proposes the “squeeze verification method,” which indirectly verifies the numerical confidence of the power time series variable under non-extreme weather conditions: by integrating CRU meteorological data, WIND energy data, and public environmental data, the ARIMA model is applied to quantify the power shortage amount caused purely by meteorological factors after stripping off the economic factors of policies in July–September 2022, which totaled 33,142 GWh, as well as the increase in thermal power generation, which amounted to 6806 GWh. Using localized emission factors, we calculated implicit emission increases: NO_x dominated pollutant growth, while extra CO₂ emissions accounted for 8.16% of annual power-sector carbon emissions. This study further uncovered synergistic environmental risks tied to emergency coal-fired power generation. These risks include elevated air pollutant and CO₂ emissions, aggravated ozone pollution, and a reinforced positive feedback loop that intensifies the extreme weather cycle. Finally, we propose targeted preventive strategies to mitigate these cascading environmental risks and ensure the sustainable development of the energy system. Full article

Climate change has increased the frequency of extreme weather events, posing a major threat to the sustainable development of agriculture and farmers’ welfare. Based on provincial meteorological data and China Family Panel Studies (CFPS) data from 2014 to 2022, this study systematically investigates the impact of climate shocks on farmers’ welfare, heterogeneity characteristics, and the buffering role of off-farm employment, using a two-way fixed-effect model. The results show that climate shocks significantly reduce farmers’ welfare, with greater welfare losses in northern regions, major grain-producing areas, and plain areas. Extreme low temperatures, extreme high temperatures, and drought are the three dominant climate hazards. In response to climate shocks, off-farm employment effectively buffers welfare losses. This study clarifies the logic of changes in farmers’ welfare and livelihood adaptation mechanisms under climate change, providing micro-empirical support for improving differentiated climate adaptation policies, strengthening agricultural risk management systems, enhancing agricultural system resilience, and promoting high-quality and sustainable agricultural development. However, constrained by the matching precision between micro-level data and meteorological indicators, future research should further refine the measurement of climate shock exposure at the individual farmer level. Full article

Against the backdrop of escalating global climate risks, reconciling economic expansion with ecological sustainability has emerged as a core challenge for urban sustainable development worldwide. This study leverages China’s Climate-Adaptive City Pilot Policy as a quasi-natural experiment and employs staggered difference-in-differences (DID) estimation on panel data covering 280 Chinese cities from 2006 to 2024 to evaluate the policy’s causal effect on urban green total factor productivity (GTFP). The empirical results yield three key findings. First, climate-adaptive urban construction delivers a significant improvement in GTFP, with a pronounced time-lagged effect: the policy exerts no statistically significant impact in the short term but generates substantial positive outcomes in the long run, verifying the dynamic implications of the strong Porter hypothesis. Second, mechanism analysis reveals two valid transmission channels through which the policy boosts GTFP, namely the expansion of firm entry (particularly the entry of non-polluting enterprises) and the agglomeration of high-skilled talents. Notably, the talent agglomeration channel is only effective in cities with advanced economic development. Dynamic tests further confirm that both firm entry and talent agglomeration responses to the policy follow consistent short-term insignificant and long-term significant patterns. Third, heterogeneous analysis demonstrates that the policy’s green growth dividends are more prominent in southern cities, non-resource-based cities, and national transportation hub cities. This study extends the existing literature on the green efficiency effects of climate adaptation policies and provides empirical evidence and differentiated policy insights for optimizing urban green transformation governance in the new era. Full article

In green credit markets, information asymmetry and corporate greenwashing increasingly undermine the efficiency of resource allocation, while traditional assessment models relying on static, self-reported environmental data fail to impose effective constraints. To address this limitation, this paper develops a dynamic corporate carbon credit evaluation framework by integrating multiple sources of physical (hard) signals and embeds it into commercial banks’ credit management systems. Anchored in multi-source power-carbon signals (e.g., carbon intensity and compliance records), the framework integrates verifiable physical metrics with ESG disclosures via a Bayesian AHP–CRITIC weighting scheme to construct a dual-dimensional classification scheme (“Credit Rating–Green Label”). It further embeds carbon credit scores into dynamic adjustments to credit limits and differentiated interest rate pricing, forming an integrated risk management mechanism. Empirically, a stratified validation strategy is adopted. Analysis based on a sample of 3327 firms shows that the proposed framework achieves a classification consistency of 81.3%, significantly outperforming both a financial-only baseline model (46.8%) and models based on voluntary carbon disclosure (61.4%). Ablation studies further confirm that physical (hard) signal indicators contribute substantially to ranking stability. Moreover, panel regression analysis, based on 36,185 firm-year observations from 3327 firms over the period 2000–2023, demonstrates that carbon credit scores have robust predictive power for future financial distress. Overall, the proposed framework offers a sustainable, data-driven approach to green credit risk management. Full article

Air pollution episodes in Metropolitan Lima pose persistent challenges for urban health protection and timely environmental decision-making. However, many machine learning approaches for air-quality prediction remain difficult to operationalize due to high latency, extensive hyperparameter tuning, and limited interpretability. This study addresses this gap by adopting an engineering-driven predictive knowledge modeling approach grounded in the Knowledge Discovery in Databases (KDD) framework to evaluate an efficient probabilistic classifier—Gaussian Naïve Bayes (GNB)—for predicting regulatory air-quality categories in Metropolitan Lima. A total of 768,185 hourly observations from SENAMHI monitoring stations covering the 2020–2025 period were analyzed, considering PM₁₀, PM_2.5, NO₂ concentrations, and the Air Quality Index (AQI). Data were preprocessed through validity checks, explicit outlier handling, and categorical encoding based on regulatory thresholds, while a time-based train–test split preserved temporal structure and prevented data leakage. The proposed model achieved strong predictive performance (global accuracy ≥ 0.925) and excellent probabilistic calibration (overall Brier Score ≈ 0.023; AQI Brier Score ≈ 0.010). These results demonstrate that GNB provides a robust, interpretable, and computationally efficient solution for operational air-quality management and early warning support, contributing to evidence-based urban environmental decision-making aligned with Sustainable Development Goal 13 (Climate Action). Full article

Anthropogenic emissions have caused the Antarctic ozone hole, a major global environmental crisis since the late 20th century. Although ozone recovery began in the early 21st century, substantial uncertainty remains regarding the timing of its return to pre-loss levels. This study innovatively develops a “model optimization–bias correction” framework based on spatial pattern (S1) and long-term trend (S2) metrics, assessing 17 Chemistry-Climate Model Initiative Phase 1 (CCMI-1) models using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis for the climate (ERA5). Results: (1) Most models accurately reproduce the Antarctic ozone’s spatial distribution and long-term trends: MRI-ESM1r1 performs best for spatial patterns (S1 = 0.80), GEOSCCM for long-term trends (S2 = 0.82); EMAC-L90MA, UMSLIMCAT, etc., show poor spatial pattern performance (S1 < 0.30), while IPSL and EMAC-L90MA have large trend biases and underperform in trend simulation (S2 < 0.10). (2) Integrating S1 and S2 scores, the Preferred Multi-Model Ensemble comprising the top eight models (PMME8) minimizes ERA5 deviation, outperforming the multi-model ensemble (MME); the Combined Nonstationary Cumulative Distribution Function matching (CNCDFm) correction of this ensemble reduces systematic bias by 15–60%. (3) Antarctic ozone recovery time shows a gradual delay following optimal model selection and bias correction. PMME-adjusted projects recovery in October 2063 (2053–2072), later than MME (2052) and PMME (2058), with inter-member uncertainty narrowing from 43 years to 19 years. Similarly, this feature is also found for September, November, and the spring mean. This study provides a reliable methodological foundation for projections of Antarctic ozone recovery and offers scientific support for the compliance assessment and policy adjustment of the Montreal Protocol, thereby advancing environmental sustainability and global ozone governance. Full article

Understanding the synergistic effects of pollution reduction (PR) and carbon mitigation (CM) and their driving factors is essential for achieving environmental improvement and dual-carbon targets. On the basis of panel data from 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2010 to 2023, this study analyzed the spatiotemporal evolution of air pollutant and carbon emissions. The synergistic effects of PR and CM were quantified using the co-control effect coordinate system and vector angle analysis, and their underlying driving mechanisms were examined using a geographically and temporally weighted regression model. Results showed that air pollutant emissions in the BTH region declined substantially over the study period, whereas carbon emissions increased in all cities, except Beijing. The spatial patterns of air pollutant and carbon emissions were largely consistent, with Tangshan being a high-emission hotspot and northern Hebei cities being low-emission areas. Most cities were in a “pollution reduction but carbon increase” stage, but the overall synergistic degree gradually improved. The synergistic effects were positively driven by green travel and technological R&D and negatively influenced by economic development, energy utilization, and transportation structure. The positive effect of industrial structure on PR and CM weakened, and spatial heterogeneity was evident. Economic development and technological R&D exerted strong influences in southern Hebei. Energy utilization and transportation structure had pronounced effects in northern Hebei. Industrial structure had remarkable effects in cities surrounding Beijing and Tianjin. Moreover, green travel demonstrated spatial heterogeneity, exerting a facilitative effect on emissions in southern Hebei cities. These findings provide policy implications for promoting the synergistic effects of PR and CM in the BTH urban agglomeration. Full article