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Climate change is considered to increase economic costs by worsening heat-related labor productivity loss. While extensive global and national research has been conducted on this topic, few studies have analyzed subnational and individual economic impacts, potentially weakening local governments' motivation to tackle climate change. Figuring out the most affected regions and labors could help climate policymakers to identify priority regions and sectors to allocate adaptation resources efficiently, and enhance stakeholder engagement. This study adopted a provincial Computable General Equilibrium model by distinguishing different labors and regions in modelling work to address the aforementioned gap. The study estimated economic costs at different level under three climate change scenarios (lower (SSP126), middle (SSP245), and higher (SSP585) warming scenario). Low-income regions located in southwest part of China (such as Guangxi and Guizhou), would experience the largest economic loss, 3.4-7.1 times higher than high-income in China by 2100 under SSP245 scenario. Additionally, wages for labors highly sensitive to heat in these regions are expected to rise, for example, by an 8.3% rise in Guangxi, driven by the rising demand for these labors. Conversely, others would experience a significant wage decrease, especially those with less sensitivity (e.g., managers). Therefore, we recommended that national financial supports be allocated more to these most affected regions and that government encourage managers provide assistance to workers vulnerable to heat., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Climate change has increasingly become a significant challenge to sustainable socio-economic development, and climate adaptation is a key issue that relevant research focuses on regional sustainable development models. By employing panel data between 2007 and 2020 from 284 Chinese prefecture-level cities, this study adopts quasi-experimental methods, including a difference-in-differences design and double dual machine learning model, to study the impact of climate adaptability on green regional sustainable development. Empirical results confirm that the pilot policy of building climate-resilient cities significantly improves urban green total-factor productivity. Difference-in-difference models (derived from entropy-weight and propensity score matching) and double dual learning models also support the improving effect of regional green total-factor productivity after policy intervention. The digital economy has strengthened the green development effect of pilot policies for building climate-adaptive cities. In addition, policy interventions to build climate-adaptive cities promote green urban development by optimizing industrial development structures and enhancing economic growth resilience. In addition, climate adaptability can also attract highly skilled talent and high-quality enterprises, facilitate science and technological progress in urban areas, and thus promoting the green development of cities in China. This study objectively evaluates the effects of climate policies and provides insights for global adaptation to climate change and optimization of public policies., Competing Interests: Declaration of competing interest The authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The economic impacts of climate-induced heat stress on pig farming have not been sufficiently investigated, despite the significant threat it poses to the industry. In this study, we coupled consecutive years of county-scale meteorological data with operational-level economic data to estimate the impact of climate-induced heat stress on the productivity of the Chinese pig industry using a stochastic frontier production function model. Based on model results, we projected the impact of heat stress on pig production in 2030 and 2050 with and without technological advances. Results showed that heat stress has a significant negative impact on pig production, with southern regions being more affected. However, considering technological advances, losses will decrease by 21 % and 52 %, respectively, compared with the scenario without technological advances. Simultaneously, we calculated the costs of climate change mitigation and adaptation and found that increasing production inputs to reduce production losses from heat stress costs one-fifth of forgoing production losses. Therefore, we recommend a positive climate change response strategy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Comprehending endangered species' spatial distribution in response to global climate change (GCC) is of great importance for formulating adaptive management, conservation, and restoration plans. However, it is regrettable that previous studies mainly focused on geoclimatic species, while neglected climate-sensitive subterranean taxa to a large extent, which clearly hampered the discovery of universal principles. In view of this, taking the endemic troglophile riverine fish Onychostoma macrolepis (Bleeker, 1871) as an example, we constructed a MaxEnt (maximum-entropy) model to predict how the spatial distribution of this endangered fish would respond to future climate changes (three Global Climate Models × two Shared Socio-economic Pathways × three future time nodes) based on painstakingly collected species occurrence data and a set of bioclimatic variables, including WorldClim and ENVIREM. Model results showed that variables related to temperature rather than precipitation were more important in determining the geographic distribution of this rare and endemic fish. In addition, the suitable areas and their distribution centroids of O. macrolepis would shrink (average: 20,901.75 km 2 ) and move toward the northeast or northwest within the study area (i.e. China). Linking our results with this species' limited dispersion potential and unique habitat requirements (i.e. karst landform is essential), we thus recommended in situ conservation to protect this relict., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m -2 ·a -1 ), with a multi-year average of 438.72 gC·m -2 ·a -1 , showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m -2 ·a -1 and 1.97gC·m -2 ·a -1 , respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In light of the challenges posed by climate change and rural revitalization, high-quality construction in rural areas is facing significant obstacles from rainstorm flooding. This study adopts a regional disaster system analysis framework and examines the characteristics of rural rainstorm flooding disasters, including the instability of the breeding environment, the hazard characteristics, and the vulnerability of the exposure. Using this framework, we explore the various causes of rainstorm flooding in rural China, assess the effectiveness of responses to these disasters, and examine the differences between urban and rural responses. Our analysis reveals that the increase in rainstorm flooding in rural China is a complex result of multiple factors, including increased risk, instability, and vulnerability. While rural areas have made some progress in monitoring and responding to rainstorm flooding disasters, they remain weaker than cities in terms of governance and disaster management systems. To address these challenges, we recommend increased attention to rural flood control and disaster reduction, stronger preparation for disaster prevention and reduction plans, enhanced rural disaster infrastructure defense, increased financial support for water conservancy projects, and stronger professional emergency response teams, disaster relief equipment, and materials. Additionally, we recommend strengthening disaster prevention and reduction education and publicity among rural residents to promote the ability to self-rescue and mutual rescue. The disastrous impact of climate change and resulting extreme weather events on rural areas has become increasingly evident in recent years. China's vast rural areas have been particularly affected by frequent rainstorm flooding disasters. This paper identifies the increase in rainstorms, the instability of the disaster breeding environment in rural areas, and the increased vulnerability of exposures in these areas as the main reasons behind the intensification of rural rainstorm flooding. To mitigate the impact of these disasters, attention should be paid to natural disaster risk management, normalized flood control, and disaster reduction in rural areas. This requires overall coordination of rural disaster prevention and reduction planning, emphasis on improving the defense capacity of rural areas, increasing financial support for defensive water conservancy projects, promoting the construction of professional emergency rescue teams in rural areas, scientifically distributing flood prevention and disaster relief equipment and materials, strengthening publicity and education on disaster prevention and reduction, and enhancing rural disaster prevention awareness and self-rescue and mutual rescue capabilities. [ABSTRACT FROM AUTHOR]

The upper reaches of the Yellow River in China, influenced by erosion of the Yellow River and tectonic activities, are prone to landslides. Therefore, it is necessary to investigate the existing landslide traces. Based on visual interpretation on high-resolution satellite images and terrain data, supplemented and validated by existing landslide records, this paper prepared the most complete and detailed landslide traces inventory in Jianzha County, Huangnan Tibetan Autonomous Prefecture, Qinghai Province, to date. The results indicate that within the study area of 1714 km², there are at least 713 landslide traces, ranging in scale from 3,556 m2 to 11.13 km², with a total area of 134.46 km². The total landslide area excluding the overlap area is 126.30 km². The overall landslide point density and area density in the study area are 0.42 km[sup -2] and 7.37% respectively. The maximum point density and maximum area density of landslide traces in the area are as high as 5.69 km-2 and 98.0% respectively. The landslides are primarily distributed in the relatively low-elevation northeastern part of Jianzha County, characterized mainly by large-scale loess landslides, with 14 landslides exceeding 1x10[sup 6] m². This inventory not only supplements the landslide trace data in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau, but also provides an important basis for subsequent landslide risk zoning, response to climate change, and landscape evolution. Additionally, it holds significant reference value for compiling landslide inventories in similar geological environments. [ABSTRACT FROM AUTHOR]

The Pearl River is one of China's large rivers, the second-largest river and the fourth-longest river in China. Its unique geography, landform, and climate conditions create unique fluvial geomorphological processes. Affected by human activities and climate change, the fluvial geomorphological processes in the Pearl River Basin have undergone significant changes in recent decades, seriously affecting the river's sustainable development. This paper critically reviews changes in fluvial geomorphological processes and analyzes influencing factors in the Pearl River Basin with a focus on possibilities for policy overhaul and strategic adjustments. [ABSTRACT FROM AUTHOR]

Climate change and human activities exert significant influence on the water–sediment relationship in arid and semi-arid regions. Therefore, comprehending the underlying mechanisms is crucial for the effective management of water and soil resources, as well as integrated watershed management. This research focuses on the Kuye River watershed (KYH_W) in the middle reaches of the Yellow River in China, along with its sub-watersheds Wangdaohengtazi (WDHT_SW) and Xinmiaosi (XM_SW). This paper utilizes the Mann–Kendall non-parametric test and the double cumulative curve method to examine the interannual trends of runoff, sediment transport, precipitation, temperature, and NDVI factors. Furthermore, the method of the slope change ratio of cumulative quantity (SCRCQ) is utilized to quantitatively evaluate the impacts and contribution rates of climate change and human activities on water–sediment changes within each watershed. The results are as follows: (1) From 1969 to 2019, the entire watershed experienced a significant decrease in both runoff and sediment transport, with 1997 marking the year of abrupt change. However, following 2012, the KYH_W and WDHT_SW exhibited a noticeable rebound in runoff. (2) Human activities predominantly contribute to the reduction in water and sediment in the watershed. (3) After the abrupt change, between 1998 and 2011, the contribution rates of climate change and human activities to the annual runoff reduction in the entire KYH_W reached 33% and 64%, respectively. Moreover, these rates for sediment transport reduction reached 26% and 74%, respectively. Subsequently, after 2012, the contribution rates of both factors to the increase in watershed runoff reached 29% and 71%, respectively. Factors other than the NDVI, within human activities, played a dominant role in augmenting the watershed's runoff. (4) Prior to 2011, changes in vegetation cover resulting from the Grain for Green Program, as measured by the NDVI, emerged as the primary factor responsible for reduced runoff in the watershed. Conversely, factors other than the NDVI assumed dominance in reducing sediment transport. The SCRCQ method offers a quantitative approach to assessing water–sediment changes. Based on this method, the study further underscores the substantial impacts of climate change and human activities on variations in runoff and sediment transport within the KYH_W in the middle reaches of the Yellow River. Notably, the water–sediment changes in the KYH_W exhibit distinct stage-wise and spatial discrepancies, which warrant increased attention in future research endeavors. [ABSTRACT FROM AUTHOR]

Exploring the complex relationship between population change and surface urban heat island (SUHI) effect has important practical significance for the ecological transformation development of shrinking cities in the context of the prevalence of urban shrinkage and the global climate change. This paper compares the population change and SUHI effect between population shrinking region (Northeast Region, NR) and population growing region (Yangtze River Delta, YRD) in China, and explores their differences in driving mechanisms, using GIS spatial analysis and Geodetector model. Our results indicated that there are significant differences in population changes and SUHI intensity between these two regions. About 72.22% of the cities in the NR were shrinking, while their SUHI intensities increased by an average of 1.69°C. On the contrary, the urban population in the YRD shows a linear growth trend, while their SUHI intensities decreased by 0.11°C on average. The results of bivariate Moran's I index also indicated that the spatial correlation between the urban population changes and the SUHI intensity changes are not significant in the above regions. Furthermore, there are significant differences in the primary drivers of SUHI variations between these two regions. In the NR, underlying surface changes, including the changes of green coverage and built-up areas, are the most important driving factors. However, atmospheric environment changes, such as carbon dioxide emission and sulfur dioxide emission, are the key drivers in the YRD. Northam's theory of three-stage urbanization and environmental Kuznets curve hypothesis are powerful to explain these differences. [ABSTRACT FROM AUTHOR]

Accelerating efforts for the Sustainable Development Goals requires understanding their synergies and trade-offs at the national and sub-national levels, which will help identify the key hurdles and opportunities to prioritize them in an indivisible manner for a country. Here, we present the importance of the 17 goals through synergy and trade-off networks. Our results reveal that 19 provinces show the highest trade-offs in SDG13 (Combating Climate Change) or SDG5 (Gender Equality) consistent with the national level, with other 12 provinces varying. 24 provinces show the highest synergies in SDG1 (No Poverty) or SDG6 (Clean Water and Sanitation) consistent with the national level, with the remaining 7 provinces varying. These common but differentiated SDG priorities reflect that to ensure a coordinated national response, China should pay more attention to the provincial situation, so that provincial governments can formulate more targeted policies in line with their own priorities towards accelerating sustainable development. The paper reveals areas of common and differentiated SDG priority at the national and subnational levels in China considering synergy and trade-off. The findings suggest that provincial governments should formulate more targeted policy aligning with national priority to achieve SDGs. [ABSTRACT FROM AUTHOR]

Sand prevention and control are the main tasks of desertification control. The MU Us Sandy Land (MUSL), one of China's four main deserts, frequently experiences droughts and has a very fragile biological environment. Climate change is the main factor leading to drought, and it may result in more serious drought situations in the future. The Temperature Vegetation Dryness Index (TVDI) was established using land surface temperature and normalized difference vegetation index data. In this paper, we investigate spatial and temporal change characteristics, future change trends, and the time-lag effect of TVDI on climate factors at different scales in MUSL from 2001 to 2020 using Sen + Mann–Kendall trend analysis, Hurstexponent, partial correlation analysis, and lag analysis methods. The results show that (1) the overall drought shows a spatial characteristic of gradually alleviating from west to east (TVDI = 0.6). A significant drying trend dominated 38.5% of the pixels in the fall (Z = 1.99), and a highly significant drying trend dominated the rest of the three seasons (Z average = 2.95) and the whole year (Z = 3.47). (2) In the future, dry autumn, winter, and the whole year will be dominated by continuous drying, and spring and summer will mainly change from dry to wet. The main relationships between winter TVDI and temperature (−0.06) and precipitation (−0.07) were negative, while evapotranspiration (0.18) showed a positive correlation. The six land use types in spring, summer, fall, and the whole year were primarily non-significantly positively correlated with temperature and evapotranspiration. (3) At the seasonal scale, the sensitive factors in spring and autumn were opposite, with spring TVDI responding quickly to precipitation (0.3 months) and being less sensitive to temperature (1.8 months) and evapotranspiration (2 months). At the interannual scale, desert land TVDI was most sensitive to precipitation (2.6 months) and least responsive to temperature (3 months). [ABSTRACT FROM AUTHOR]

Carbon Intensity Constraint Policies (CICPs) are vital for addressing climate change challenges and advancing sustainable development. Since 2010, China has rolled out three five-year CICPs. However, there is limited understanding of their impact on carbon emission performance (CEP). Addressing this, this study pioneers the exploration of the CICP's impact on China's CEP. Drawing from government intervention and green paradox theories, this study highlights a concerning scenario: local governments achieve emission targets via excessive intervention. For deeper insights, this study melds the overall technology frontier concept with a non-radial, non-angle directional distance function, introducing a novel efficiency model rooted in the Data Envelopment Analysis (DEA) method. This offers a CEP measure across 30 Chinese provincial regions from 2002 to 2019. Using the quasi-difference-in-differences (quasi-DID) and moderated mediation models, this study ascertains the presence of the green paradox, uncovers its reasons, and suggests mitigation strategies. The results indicate that high government intervention diminishes CEP. This negative effect intensifies under greater regional fiscal pressure. Alarmingly, local authorities' eagerness to meet targets shows a counterproductive, inverted N-shaped trend regarding CICPs' time-based influence on regional CEP. Moreover, the impact varies based on regional economic development levels and stages. This study has ensured the robustness of the findings via parallel trend tests, parallel exclusion policies, a strengthened quasi-DID framework, and diverse control variable configurations. This study underscores the need for more balanced government intervention. It offers valuable policy insights, guiding China's upcoming CICP phase to realize the ambition of peaking carbon by 2030 and achieving carbon neutrality by 2060., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financialinterestsor personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49-0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67-0.95 for climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The Tarim Basin is a large inland arid basin in the arid region of northwest China and has been experiencing significant "warming and wetting" since 1987. As a result, the purpose of this paper is to determine whether the climate transition phenomenon occurred in the Tarim Basin as well as the role of atmospheric circulation in this process. We use meteorological data and atmospheric circulation indexes to study the seasonal trends of climate change in this region from 1987 to 2020 to understand how they are affected by atmospheric circulation. The findings show that, from 1987 to 2020, the Tarim Basin experienced significant warming and wetting; with the exception of the winter scale, all other seasonal scales exhibited a clear warming and wetting trend. From the perspective of spatial distribution, most of the areas showed a significant warming trend, and the warming amplitude around the basin is greater than that in the central area of the basin. However, there are significant regional differences in precipitation change rates. Meanwhile, wavelet analysis shows that there is a significant oscillation period of 17–20 years between climate change and the atmospheric circulation index during 1987–2020. The correlation analysis shows that the Pacific decadal oscillation (PDO) and El Niño-Southern Oscillation (ENSO) are the main influencing factors of climate change in the Tarim Basin at different seasonal scales, while the teleconnection of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) is low and the PDO dominates the summer and autumn temperature changes in the Tarim Basin. The research results of this paper show that, despite the warming and wetting trends since 1987 in the Tarim Basin, the climate type did not change. From 1987 to 2020, the main teleconnection factors of climate change in the Tarim Basin were PDO and ENSO. [ABSTRACT FROM AUTHOR]

To better address climate change and environmental pollution, it is of great practical significance to comprehensively evaluate the impact of low-carbon energy transition policies on carbon emission reduction. This paper conducts a policy quantitative evaluation of low-carbon energy transition policies from three dimensions: policy intensity, policy objective, and policy instrument. On this basis, the ridge regression model is used to analyze the carbon emission reduction effect of policies. The results show that (1) changes in policy quantities mainly cause the evolution of total policy effectiveness. The government's emphasis on low-carbon energy transition has fluctuated. (2) The two policy objectives of optimizing the use of fossil energy and developing a low-carbon concept are less synergistic with other policy objectives and neither has a significant impact on carbon reduction. (3) The government prefers to adopt command control policy instruments, but their actual carbon emission reduction effects are inferior to that of economic incentive policy instruments. These findings can provide a decision-making basis for the government to further formulate and optimize policies. [ABSTRACT FROM AUTHOR]

To depict the wet (April with a soil water content, SWC, of 37 %) and dry (October with a SWC of 24.8 %) seasonal changes in the water use and physiological response of a Eucalyptus urophylla plantation in subtropical South China characterized by monsoon climate, the whole-year (June 2012 to May 2013) transpiration of E. urophylla was monitored using the TDP method. Daily transpiration (ET) in October averaged 5.7 ± 2.9 kg d-1 and was 58.0 % higher than that in April (3.6 ± 2.3 kg d-1). The difference is consistent with that of the radiation and evaporative demand of the two months, while the nocturnal transpiration (ET-NOC) in the wet season (0.18 ± 0.021 kg d-1) was almost twice that in the dry season (0.11 ± 0.01 kg d-1). Trees displayed a higher stomatal conductance (GS) (53.4-144.5 mmol m-2 s-1) in the wet season and a lower GS (45.7-89.5 mmol m-2 s-1) in the dry season. The leaf-soil water potentials (ΨL) of the two months (April and October) were -0.62 ± 0.66 and -1.22 ± 0.10 MPa, respectively. A boundary line analysis demonstrated that the slight improvement in the GS by SWC in wet season was offset by a significant decrease in D, and the slope of GS sensitivity to D (dGS/dlnD) in response to GSref (references GS at D = 1 kPa) was affected by the variance of radiation instead of SWC. Specific hydraulic conductivity (ks) of trees of different sizes decreased by 45.3-65.6 % from the wet to the dry season. Combining the decreased maximum reference GS at D = 1 kPa (GSref-max) by 22.4 % with the constant max GS (GSmax) when ΨL < -1.2 MPa, we shed some light on the mechanism underlying the high water-use efficiency (WUE) of this Eucalyptus specie. With a slight change in GSref-max and high sensitivity of ks to decreasing ΨL, large trees used water more efficiently than small ones did. In addition, the -m in the dry season (0.53 ± 0.007) was lower than that in the wet season (0.58 ± 0.01) due to the difference in the ratio of GS to the boundary layer conductance (gb) in the two months. The negative relationship between -m (except when light is limited) and Q proved to be a plastic response to environmental changes for E. urophylla but did not change with decreased ks as expected. [ABSTRACT FROM AUTHOR]

Concentrations of leaf nitrogen (N) and phosphorus (P) are key leaf traits in ecosystem functioning and dynamics. Foliar stoichiometry varies remarkably among life forms. However, previous studies have focused on trees and grasses, leaving the knowledge gap for the stoichiometric patterns of shrubs. In this study, we explored the intra- and interspecific variations of leaf N and P concentration in relation to climate, soil property and evolutionary history based on 1486 samples composed of 163 shrub species from 361 shrubland sites in northern China expanding 46.1° (86.7-132.8° E) in longitude and 19.8° (32.6-52.4° N) in latitude. The results showed that leaf N concentration decreased with precipitation, leaf P concentration decreased with temperature and increased with precipitation and soil P concentration. Both leaf N and P concentrations were phylogenetically conserved, but leaf P concentration was less conserved than leaf N concentration. At community level, climates explained more interspecific, while soil nutrient explained more intraspecific, variation of leaf nutrient concentrations. These results suggested that leaf N and P concentrations responded to climate, soil, and phylogeny in different ways. Climate influenced the community chemical traits through the shift in species composition, whereas soil directly influenced the community chemical traits. [ABSTRACT FROM AUTHOR]

The rapid development of the aviation industry significantly impacts global climate change. The carbon Emissions Trading Scheme (ETS) and Sustainable Aviation Fuels (SAFs) are considered two essential ways for the aviation industry to reduce emissions. This paper uses China-foreign routes, the fastest growing region in the world, as an example to calculate the actual emissions (CO2, CO, HC, NOx, SO2, and PM2.5), calculate the Carbon Dioxide Equivalent Concentration (CDEC) using the Aviation FAIR-GWP concentration method, to compare the role of the ETS and SAFs in controlling emissions until the year 2100. The results show that SAFs are more effective in controlling the pollutant emissions of the aircraft than ETS, as ETS mainly covers CO2 and ignores other greenhouse gases. However, the best way is to combine SAFs and ETS; the combined effect is much better than SAFs alone or ETS alone. The research results provide systematic suggestions for the aviation industry to reduce emissions effectively. [ABSTRACT FROM AUTHOR]

The rainfall distribution in southwest China is uneven, and the rainstorm threshold cannot use in a unified standard. This paper synthesizes a calculation method for the extremely heavy precipitation threshold and the provision of the rainstorm threshold in meteorological operation. It calculates the daily precipitation rainstorm threshold at 400 national ground stations in southwest China. The rainstorm events from 1961 to 2021 were statistically analyzed using the rainstorm threshold and analyzing the spatial-temporal variation characteristics. The results show that the number of single-station rainstorm events and the average precipitation of single-station rainstorm events in southwest China decreased from east to west. The number and frequency of single-station rainstorm events in Guizhou, Sichuan, Tibet, and Chongqing are increasing, while the number of single-station rainstorm events in Yunnan is decreasing. There is no apparent spatial distribution pattern for the continuous rainstorm events in the southwest region. From 1961 to 2021, the number and frequency of rainstorm events at a single station in southwest China followed an upward trend. The number of rainstorm events at a single station increased by 16.7 times · (10a)−1, and the frequency of rainstorms increased by 9.9% · (10a)−1. The continuous rainstorm events show an increasing trend, with an increase of 0.1 times · (10a)−1. Using the rainstorm threshold in southwest China, the early warning threshold for rainstorm disasters can be adjusted. The temporal and spatial characteristics of rainstorm events since 1961 can analyze the changes occurring in rainstorm events under global warming and provide data to support the response of southwest China to climate change. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

Climate change is the dominant factor affecting the hydrological process, it is of great significance to simulate and predict its influence on water resources management, socio-economic activities, and sustainable development in the future. In this paper, the Xiying River Basin was taken as the study area, China Atmospheric Assimilation Driven Data Set (CMADS) and observation data from the Jiutiaoling station were used to simulate runoff of the SWAT model and calibrate and verify model parameters. On this basis, runoff change of the basin under the future climate scenario of CMIP6 was predicted. Our research shows that: (1) The contribution rates of climate change and human activities to runoff increase of the Xiying River are 89.17% and 10.83%, respectively. Climate change is the most important factor affecting runoff change of the Xiying River. (2) In these three different emission scenarios of SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6 climate model, the average temperature increased by 0.61, 1.09 and 1.74 °C, respectively, in the Xiying River Basin from 2017 to 2050. Average precipitation increased by 14.36, 66.88, and 142.73 mm, respectively, and runoff increased by 15, 24, and 35 million m³, respectively. The effect of climate change on runoff will continue to deepen in the future. [ABSTRACT FROM AUTHOR]

As a raw material for clean energy supply for the new generation, the soybean is conducive to the realization of global energy transition and sustainable development in the context of "carbon neutrality". However, global warming has been affecting soybean yields in recent years. How to clarify the correlation between meteorological factors and soybean yields, so as to ensure the security of soybean growth and development and the stability of renewable energy development, is a key concern of the government and academia. Based on the data of temperature, precipitation, sunshine duration and active accumulated temperature during the soybean growing season in Hulunbuir, Inner Mongolia Autonomous Region from 1951 to 2019, and soybean yield data of the city from 1985 to 2019, this paper adopted statistical methods such as the Trend Analysis Method, the Rescaled Range Analysis Method and so on to analyze the trends of yield changes, characteristics of abrupt changes and periodic patterns of climate factors and soybean yields in Hulunbuir. A Pearson Correlation Analysis and a Grey Relation Analysis were used to explore the correlation between climatic factors and soybean yields, followed by a comprehensive impact model of the combined effect of temperature and precipitation on soybean yields established by the Method of Integral Regression. The results showed that temperature and active accumulated temperature are the dominant factors affecting soybean yields in Hulunbuir, while the decrease in precipitation is unfavorable to the improvement of soybean yields. Meanwhile, temperature and precipitation have different effects on the growth and development of the soybean at different stages. The conclusion of this paper is of great practical significance for Hulunbuir to promote the sustainable development of clean energy. [ABSTRACT FROM AUTHOR]

Over-exploitation of groundwater due to intensive irrigation and anticipated climate change pose severe threats to the water and food security worldwide, particularly in the North China Plain (NCP). Limited irrigation has been recognized as an effective way to improve crop water productivity and slow the rapid decline of groundwater levels. Whether optimized limited irrigation strategies could achieve a balance between groundwater pumping and grain production in the NCP under future climate change deserves further study. In this study, an improved Soil and Water Assessment Tool (SWAT) model was used to simulate climate change impacts on shallow groundwater levels and crop production under limited irrigation strategies to suggest optimal irrigation management practices under future climate conditions in the NCP. The simulations of eleven limited irrigation strategies for winter wheat with targeted irrigations at different growth stages and with irrigated or rainfed summer maize were compared with future business-as-usual management. Climate change impacts showed that mean wheat (maize) yield under adequate irrigation was expected to increase by 13.2% (4.9%) during the middle time period (2041-2070) and by 11.2% (4.6%) during the late time period (2071-2100) under three SSPs compared to the historical period (1971-2000). Mean decline rate of shallow groundwater level slowed by approximately 1 m a -1 during the entire future period (2041-2100) under three SSPs with a greater reduction for SSP5-8.5. The average contribution rate of future climate toward the balance of shallow groundwater pumping and replenishment was 62.9%. Based on the simulated crop yields and decline rate of shallow groundwater level under the future climate, the most appropriate limited irrigation was achieved by applying irrigation during the jointing stage of wheat with rainfed maize, which could achieve the groundwater recovery and sustainable food production., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Industrial structure adjustment is an effective way to reduce carbon emissions. When adjusting the industrial structure, changes in the input and output of industrial sectors in one region may cause changes in industrial sectors of other regions, resulting in inter-regional industrial linkages. What is the impact of industrial structure adjustment on the linkages, and how does it impact climate change? The modified gravity model combines the influence and inductive coefficients to build the carbon emission industrial spatial linkage network (ISL) using social network analysis (SNA) in this work with the data of China's 2008-2019 provincial carbon emissions. The impact of industrial structure adjustment on carbon emission industrial spatial linkage network and its effect on climate change mitigation are explored with the help of the STIRPAT model and quality assurance procedure (QAP). The results of this study are as follows: (1) The provincial carbon emissions under industrial spatial linkages show significant network characteristics, the network density increased from 0.1437 in 2008 to 0.323 in 2019. (2) There is a significant spatial spillover effect in the industrial spatial linkage network (2019) centered on Shandong and Jiangsu. The provinces can be divided into 4 blocks, which play different roles in the network. (3) The industrial structure upgrading can significantly promote the carbon emission industrial linkage network and mitigate climate change. (4) The industrial structure rationalization can promote the development of the network and mitigate climate change in the early and late development stages of carbon emissions industrial linkage. In addition, it is necessary for the government to pay more attention to the development level of the carbon emission spatial industrial linkage network when implementing industrial structure adjustment in response to climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Climate change has impacted the distribution and abundance of numerous plant and animal species during the last century. Orchidaceae is one of the largest yet most threatened families of flowering plants. However, how the geographical distribution of orchids will respond to climate change is largely unknown. Habenaria and Calanthe are among the largest terrestrial orchid genera in China and around the world. In this paper, we modeled the potential distribution of eight Habenaria species and ten Calanthe species in China under the near-current period (1970-2000) and the future period (2081-2100) to test the following two hypotheses: 1) narrow-ranged species are more vulnerable to climate change than wide-ranged species; 2) niche overlap between species is positively correlated with their phylogenetic relatedness. Our results showed that most Habenaria species will expand their ranges, although the climatic space at the southern edge will be lost for most Habenaria species. In contrast, most Calanthe species will shrink their ranges dramatically. Contrasting range changes between Habenaria and Calanthe species may be explained by their differences in climate-adaptive traits such as underground storage organs and evergreen/deciduous habits. Habenaria species are predicted to generally shift northwards and to higher elevations in the future, while Calanthe species are predicted to shift westwards and to higher elevations. The mean niche overlap among Calanthe species was higher than that of Habenaria species. No significant relationship between niche overlap and phylogenetic distance was detected for both Habenaria and Calanthe species. Species range changes in the future was also not correlated with their near current range sizes for both Habenaria and Calanthe. The results of this study suggest that the current conservation status of both Habenaria and Calanthe species should be adjusted. Our study highlights the importance of considering climate-adaptive traits in understanding the responses of orchid taxa to future climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

To detect potential impacts of climate change on the distribution of common echinoderm species in the Yellow Sea (YS) and East China Sea (ECS), species distribution models (SDMs) were applied. Ensemble SDMs were constructed and were in good model performance for six of the eight selected common echinoderm species. Under future climate scenarios, the brittle stars Ophiopholis mirabilis, Amphioplus depressus and the sea cucumber Protankyra bidentata were projected to expand in the southwestern areas of the YS, the ECS, and the coastal areas of the YS and ECS, respectively; the brittle stars Stegophiura sladeni, Amphiura digitula and Amphiura vadicola will likely contract their ranges in the south distribution areas and expand in the north, showing a northward movement trend. Temperature was the most important environmental variable influencing the distribution of the latter three echinoderms. Our findings will improve our understanding of the impacts of climate change on marine species distributions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

With the intensification of global climate change, the frequency of wildfires has markedly increased, presenting an urgent challenge in assessing tripping failures for power systems. This paper proposes an innovative method to evaluate the spatial wildfire-induced tripping risk of transmission lines based on a flame combustion model. Firstly, Bayes theory is employed to assess the spatial probability of wildfire occurrence. Subsequently, Wang Zhengfei's flame combustion model is utilized to estimate the potential flame height of wildfires along the transmission corridor. Thirdly, the insulation breakdown risk of the transmission line is calculated based on the relative height difference between the flame and the transmission line. Finally, the spatial wildfire-induced tripping risk of the transmission line is then determined by combining the wildfire occurrence probability and the insulation breakdown risk. A case study conducted in Guizhou province, China validates the accuracy of the proposed model. Utilizing ArcGIS, the wildfire occurrence probability distribution in Guizhou is visualized to enhance the efficiency of operation and maintenance. The results indicate that over 80% of wildfire incidents occurred in areas with occurrence probabilities exceeding 50%. [ABSTRACT FROM AUTHOR]

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Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle. [ABSTRACT FROM AUTHOR]

Climate change is an important issue for cities today and in the future. At present, China has a large population and complex climate conditions, and cities are also vulnerable to the adverse effects of climate change (Tian, Environ Sustain Dev 6: 153-155 2020). Three-dimensional greening can not only improve the green space system of a city but also have a far-reaching impact on the ecology, image, and economic benefits of a city. Therefore, the study of urban three-dimensional greening is an effective means to deal with climate change strategies. By exploring the influence of traditional greening and three-dimensional greening on Local Climate in Beilin District of Xi'an, Shaanxi Province, the mechanism of three-dimensional greening on urban ecological environment was discussed, and the ecological theory, urban three-dimensional greening theory, and urban local climate zone (LCZ) were referred to. Based on the methods of national climate monitoring, ENVI-met simulation, and field independent measurement, this paper selected a research sample site in the east section of Jianshe Road, Beilin District, Xi'an City, Shaanxi Province, China, and applied ENVI-met software to simulate the thermal stress relationship among building exterior surfaces, plants, and air in the street; quantified the overall ecology of the area; and used measuring instruments. The influence of different types of greening in the base on the site temperature, humidity, CO2 (carbon dioxide) concentration, wind speed, and other climate factors data was, respectively, measured and analyzed. The grid analysis was used to compare the traditional greening and three-dimensional greening, then the numerical differences of each impact factor were sorted out, and the effect of three-dimensional greening on the improvement of urban ecological environment was discussed by analyzing the climate factors with greater impact. The results show that (1) three-dimensional greening plus traditional greening is the most beneficial mode; (2) in the same environment, according to the parameter of 1.5 m from the ground in the model environment, it can be seen that the temperature of the space treated with three-dimensional greening of buildings is reduced by 3.5–3.6 ℃ compared with the control group, the relative humidity is different by 7–8%, the CO2 concentration is reduced by about 5%, and the spatial wind speed is relatively small. (3) When the urban green coverage rate is more than 40%, the improvement of temperature is more obvious, if it reaches 50%, the cool phenomenon in summer can be fundamentally changed. From the perspective of human perception, the PMV index increased by 0.27 on average. This paper discusses and analyzes the three-dimensional greening of urban streets in Beilin District, Xi'an City, Shaanxi Province, China, and studies its influence on urban ecology to different degrees. The conclusions are as follows: Different types of greening have different degrees of influence on urban climate. Meanwhile, the experimental results of this paper show that in cities like Xi'an, Shaanxi Province, China, where summer is hot, adding three-dimensional greening to traditional street greening can significantly improve the environmental microclimate, which is an effective means to cope with climate change, improve the site environment, and stabilize the urban ecosystem. [ABSTRACT FROM AUTHOR]

The Beijing–Tianjin–Hebei region in China is experiencing a serious ecological water scarcity problem in the context of climate warming and drying. There is an urgent need for practical adaptation measures to cope with the adverse impacts of climate change and provide a scientific basis for urban water supply planning, water resource management, and policy formulation. Urban ecological water can maintain the structure and function of urban ecosystems, both as an environmental element and as a resource. Current research lacks quantitative analysis of the impact of regional meteorological factors on ecological water use at the small and medium scales. Based on the meteorological data and statistical data of water resources in the Beijing–Tianjin–Hebei (BTH) region, this paper analyzed the trend of climate change and established an ecological climatic water model using gray correlation analysis, polynomial simulation, and singular spectrum analysis to predict the ecological water consumption. And, we assessed the climatic sensitivity of ecological water use and estimated the future ecological climatic water use in the BTH region based on four climate scenarios' data. The results showed that the average multi-year temperature was 13.2 °C with a clear upward trend from 1991 to 2020 in the BTH region. The multi-year average precipitation was 517.1 mm, with a clear shift in the period of abundance and desiccation. Ecological climatic water modeling showed that a 1 °C increase in temperature will increase ecological water use by 0.73 × 108 m3~1.09 × 108 m3 in the BTH region; for a 100 mm increase in precipitation, ecological water use will decrease by 0.49 × 108 m3~0.88 × 108 m3; under the four climate scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5, the regional ecological climatic water use will be 5.14 × 108 m3, 6.64 × 108 m3, 7.82 × 108 m3, and 9.06 × 108 m3 in 2035, respectively; and in 2050, the ecological climatic water use will be 8.16 × 108 m3, 9.75 × 108 m3, 10.71 × 108 m3, and 12.41 × 108 m3, respectively. The methodology and results of this study will support the quantification of climate change impacts on ecological water use in the BTH region and serve as a theoretical basis for future research on ecological water use adaptation to climate change. This study can provide a basis for the development of the overall planning of urban ecological water supply, and at the same time, it can lay a foundation for the study of measures to adapt to climate change by ecological water use. [ABSTRACT FROM AUTHOR]

This paper proposes a conceptual and analytical framework of states' legitimacy-seeking to comprehensively investigate the motivation behind China's climate and environment policy. While previous research has largely overlooked political factors that underlie China's climate policy, this paper argues that these factors are crucial in understanding China's policy changes, which are evident at both domestic and international levels. By examining sources such as government documents, leaders' speeches and authoritative literature, this study contends that China's climate change initiatives are part of a broader effort to enhance domestic and international legitimacy. The issue of climate change has become highly politicised in China under the leadership of President Xi Jinping, and it serves as a crucial test of the ruling party and the state's capacity to govern effectively. As such, legitimacy-seeking is the key driver that links China's domestic measures and international commitments. [ABSTRACT FROM AUTHOR]

Purpose: After almost 10 years, people wonder if green finance has been able to attain its objectives in terms of controlling climate change. Persistent global warming and climate deregulation manifested by melting glaciers, droughts and floods, are all of these determinants that have called into question the efficiency of green finance. Design/methodology/approach: Green finance is a way to support climate action through investments. It has proven that this is a viable financial instrument and that it can be used by governments and private companies to plan for the future of our planet. Findings: Based on an analysis of articles published in top international journals from 2016 to 2022, about the relationship between green technology and financial services in China, this paper aims to present an overview of green finance, its importance for the planet, its objectives and its instruments. Research limitations/implications: This study's contribution is to shed light on the aspects that may have limited its effectiveness, such as the absence of incentives, the absence of climate costs and above all the absence of finance green standards. Originality/value: The results have shown that there is still a significant gap in green finance before inclusive green growth can be achieved. Inclusive green growth. All stakeholders need to increase the level of investment in green finance. The green investment financing gap is the result of inconsistencies in sustainability and policies. Therefore, governments must intervene to impose appropriate policies and regulations to compel the financial sector to engage in sustainable development. All of these factors make the concept of green finance just an illusion. [ABSTRACT FROM AUTHOR]

Steel sheeting is usually provided at the upper flange of purlin in steel roof with screw connections. The resulting diaphragm effect provided by the sheeting enhances the buckling resistance of the purlin. However, this diaphragm effect will be reduced due to any abnormality in the screw connection caused by wind-induced fatigue particularly from typhoon. This would reduce the buckling resistance of the purlin with sheeting (the purlin-sheeting system), and it would affect subsequent condition evaluation during their service life. This problem is addressed in this paper with a framework of time-dependent reliability evaluation of the buckling limit state of the purlin-sheeting system. The buckling resistance reduction due to the degradation of the diaphragm effect from typhoon is taken into account in the resistance model. The randomness of wind intensity and landfall occurrence of typhoon during the service life of purlin are also accounted for in the wind load effect model. The time-dependent reliability of the purlin at different positions of a roof is studied. Then the variation of reliability of the system due to the effect of climatic changes in the design service life is discussed. The proposed strategy is then applied to the evaluation of a purlin-sheeting system in eleven cities in China with typhoon occurrences with some useful results for future studies in the provisions of the design code. [ABSTRACT FROM AUTHOR]

A process-oriented niche specification (PONS) model was constructed to quantify climatic controls on the distribution of ecosystems, based on the vegetation map of China. PONS uses general hypotheses about bioclimatic controls to provide a "bridge" between statistical niche models and more complex process-based models. Canonical correspondence analysis provided an overview of relationships between the abundances of 55 plant communities in 0.1° grid cells and associated mean values of 20 predictor variables. Of these, GDD (accumulated degree days above 0 ° C) Cramer--Prentice α (an estimate of the ratio of actual to equilibrium evapotranspiration) and mGDD5 (mean temperature during the period above 5 ° C) showed the greatest predictive power. These three variables were used to develop generalized linear models for the probability of occurrence of 16 vegetation classes, aggregated from the original 55 types by k-means clustering according to bioclimatic similarity. Each class was hypothesized to possess a unimodal relationship to each bioclimate variable, independently of the other variables. A simple calibration was used to generate vegetation maps from the predicted probabilities of the classes. Modelled and observed vegetation maps showed good to excellent agreement (κ = 0.745). A sensitivity study examined modelled responses of vegetation distribution to spatially uniform changes in temperature, precipitation and [CO2], the latter included via an offset to α (based on an independent, data-based light use efficiency model for forest net primary production). Warming shifted the boundaries of most vegetation classes northward and westward while temperate steppe and desert replaced alpine tundra and steppe in the southeast of the Tibetan Plateau. Increased precipitation expanded mesic vegetation at the expense of xeric vegetation. The effect of [CO2] doubling was roughly equivalent to increasing precipitation by ~ 30%, favouring woody vegetation types, particularly in northern China. Agricultural zones in northern China responded most strongly to warming, but also benefited from increases in precipitation and [CO2]. These results broadly conform to previously published findings made with the process-based model BIOME4, but they add regional detail and realism and extend the earlier results to include cropping systems. They provide a potential basis for a broad-scale assessment of global change impacts on natural and managed ecosystems. [ABSTRACT FROM AUTHOR]

Based on four field investigations during 2003 and 2009 along the Yellow River mainstream, we examined the distributions, seasonal variations and transport features of organic carbon, with a focus on the impacts of human activities (reservoir construction and regulation scheme). The results showed that organic carbon transport processes in the Yellow River were different from other major rivers. Particulate organic carbon (POC) dominated in the Yellow River and it mainly originated from the Loess Plateau. POC levels in suspended sediment (POC %) ranged between 0.25% and 2.20% and more than 80%of POC concentrated in the particles with grain size smaller than 16 µm. On time scale, dissolved organic carbon (DOC) correlated negatively with discharges, indicating the influence of dilution effect. Along the river on spatial scales, DOC in the Qinghai-Tibet Plateau was closely related with temperature while DOC in the Loess Plateau showed the concentration effect, due to the abundant human input and the high ratio of evaporation to precipitation. Organic carbon in the Yellow River was very refractory and about 90% of POC and 70% of DOC cannot be degraded. Due to the high turbidity, the Yellow River suffers more impacts from the reservoirs in the transport of total suspended solids (TSS) and organic carbon. Ratios of DOC/POC ranged between 2.0 and 12 in the reservoirs and organic carbon was mainly in the dissolved from. POC deposited in the reservoirs of the Yellow River achieved 0.0033 Gta-1, about 8 times its annual POC flux discharged to the ocean. During the 2008 Water and Sediment Regulation (WSR) period, DOC and POC fluxes was as high as 1.1x1010 g and 2.2x1011 g respectively, accounting for 35% of annual DOC flux and 56% of the annual POC flux to the ocean. Discharges and material fluxes to the ocean decline sharply due to the reservoir construction while large amounts of water and sediment are transported to the ocean in such a short WSR period. These two human disturbances totally altered the processes of substance transport in the Yellow River, and may change the water chemical characteristics in the coastal zones. [ABSTRACT FROM AUTHOR]

Soil carbon sequestration plays an essential role in mitigating CO2 increases and the subsequently global greenhouse effect. The storages and dynamics of soil organic carbon (SOC) of 0-30 cm soil depth in different landscape types including beaches, reservoir and pond, reed wetland, forest wetland, bush wetland, farmland, building land, bare land (severe saline land) and salt field in the modern Yellow River Delta (YRD), were studied based on the data of the regional survey and laboratory analysis. The landscape types were classified by the interpretation of remote sensing images of 2000 and 2009, which was calibrated by field survey results. The results revealed an increase of 10.59km² in the modem YRD area from 2000 to 2009. The SOC density varied ranging from 0.73 kgm-2 to 21.60 kgm-2 at depth of 30 cm. There were ∼3.97 x 106 t and 3.98 x 106 t SOC stored in the YRD in 2000 and 2009, respectively. The SOC storages changed greatly in beaches, bush wetland, farm land and salt field which were affected dominantly by anthropogenic activities. The area of the YRD increased greatly within 10 yr, however, the small increase of SOC storage in the region was observed due to landscape changes, indicating that the modern YRD was a potential carbon sink and anthropogenic activity was a key factor for SOC change. [ABSTRACT FROM AUTHOR]

At the 75th session of the United Nations General Assembly (UNGA) in 2020, China put forward the goal of peaking carbon dioxide emissions by 2030 and achieving carbon neutrality by 2060, a move to lead global response to climate change that has attracted wide attention and hot comments at home and abroad. Therefore, it is of great practical significance and academic value to explore ways of achieving carbon peaking ahead of schedule and study the macroeconomic effect. This paper, based on Energy, Environment and Economy recursive dynamic computable general equilibrium model (TECGE), a dynamic computable general equilibrium model, carries out a quantitative analysis of the effect of strengthening carbon peaking commitment on China’s future macro economy. By setting up four scenarios, namely carbon peaking of 10.8 billion tons, 10.7 billion tons, 10.58 billion tons and 10.36 billion tons in 2030, 2027, 2025, and 2023, it examines the effects of carbon peaking ahead of schedule and carbon peaking in 2030 on macro economy. The findings show that, compared with the 2030 benchmark, the more ahead of schedule carbon peaking is achieved, the higher the carbon tax prices, and that though GDP and other macroeconomic variables, such as aggregate consumption, aggregate imports and exports decline, the share of the tertiary industry increases. That is, the more ahead of schedule carbon peaking is achieved, the more macroeconomic variables decline, and the more the share of the tertiary industry rises. This paper, using computable general equilibrium (CGE) model to conduct a quantitative analysis of the macroeconomic effect, makes policy recommendations for carbon peaking ahead of schedule and high-quality economic development. [ABSTRACT FROM AUTHOR]

Leaf wax molecular and isotopic ratios are generally considered robust isotopic pale-ohydrologic proxies. Here we evaluate the proxy value of the molecular distributions and hydrogen isotopic compositions of long chain n-alkanes (δDalk ) in surface peats collected from peatlands across a range of annual air temperatures from 1 to 15°C and a range of annual mean precipitation from 720 to 2070 mm in the monsoonal region of China. The alkane ratios (ACL and CPI) and δDalk values show relatively large variations in multiple samples from a single site, highlighting the complexity of these ratios at a small spatial scale. In the montane Zoigê peatland, the apparent fractiona-tion between precipitation and δDalk is more positive than in the other six sites, which is possibly an effect of the higher conductivity of the water in this high elevation site (3500 m a.s.l.). At a larger spatial scale, the site-averaged CPI ratios and the δDalk values of n-C29 and n-C31 alkanes show significant correlation with the air temperature and precipitation. These results support the application of the CPI ratio and the δDalk ratios of n-C29 and n-C31 alkanes as sensitive paleohydrologic proxies on millennial and larger timescales. [ABSTRACT FROM AUTHOR]

Henan Province is the birthplace of Chinese civilization and one of the earliest human settlements, which means that the area has an important national cultural heritage. Traditional villages are an important facet of this cultural heritage, and studying their spatiotemporal characteristics in different periods has important theoretical and practical significance for the sustainable development and protection of cultural heritage in the region. This paper takes the traditional villages of the Yellow River basin in Henan Province that were formed before 1919 as the research object. Information on the ancient river was obtained through a literature search as well as via field research, and the important tributaries of the Yellow River in different periods were mapped using ArcGIS 10.0 software. The nearest neighbor index, kernel density estimation, standard deviation ellipse and other methods were adopted to analyze the spatiotemporal characteristics of the traditional villages. The factors that influenced the evolution of traditional villages were explored in depth by combining changes in the course of the river and water conservancy projects. The results show that the formation of traditional villages along the Yellow River in Henan Province has experienced a historical track of growth, contraction, growth and prosperity and stability. The traditional villages along the Yellow River in Henan Province generally show a clustered pattern, forming a dense concentration of traditional villages in the middle reaches of the Yellow River. The center of gravity shows a migration trend from southeast to northwest. In terms of influencing factors, the spatiotemporal evolution of the relationship between villages and their distance to water is closely related to climatic fluctuations, changes in channel, water conservancy projects and social and cultural factors. This paper deepens our understanding of the relationship between traditional village evolution and watersheds by improving the consistency between village spatial distribution and historical geography and provides a useful theoretical reference for the sustainable development of China's traditional villages. [ABSTRACT FROM AUTHOR]

We estimate the relationship between high‐temperature exposure and land transaction using daily transaction data from 2013 to 2018 in China. Standard hedonic price method is used to perform multidimensional fixed effects regression on 458,564 transaction samples. This study shows that each additional day of the extremely high temperature increases the average bid price by 0.6%, which is equivalent to an additional annual increase of 15.018 billion yuan in land transaction costs. The effect of high temperature on land prices is considerably stronger for residential and commercial lands. We further find that high‐temperature‐induced changes of mood and cognition play a role. Extreme gradient boosting (XGBoost) algorithm is used to calculate price deviation, which overcome missing eigenvalues and reduce nonlinear measurement error. Our findings indicate that heat can make investors more aggressive, which occurs when bidders continue to raise prices to win. The number of bidding behavior will not drop due to the hidden costs of land transactions. Specially, bidding experience offsets the cognitive output bias. This study contributes to the behavioral finance and decision‐making literature, helps investors make better investment management decisions, and alleviates the negative effect of land transaction premium on the real estate market. Plain Language Summary: Extreme temperatures are becoming more frequent due to climate change. Extreme temperature can affect people's cognitive performance. Using large‐scale land transaction data and a fixed‐effects model, this paper analyzes the impact of extreme temperature on decision‐making. Results show that each additional day of extremely high temperature increases the average bid price by 0.6%, which is equivalent to an additional annual increase of 15.018 billion yuan in land transaction costs. This paper analyzes the impact of extreme temperature on human cognitive performance from the perspective of e climate change and uses land transaction behavior as a measure of decision‐making to reveal the mechanism of climate change affecting cognitive performance. Key Points: Each additional day of the extremely high temperature increases the average bid price by 0.6%Heat can make investors more aggressive, which occurs when bidders continue to raise prices to winBidding experience offsets the cognitive output bias [ABSTRACT FROM AUTHOR]

Climate change has been a widely concerned issue for decades. As the key policy target, the economic impact caused by climate change has received general attention from scholars and governments around the world. For the number of literatures is huge and the relationships among the literatures are not clear, we aim to clarify the research hotpots and the research trends of current literatures and provide inspiration for the development directions of future research in this paper. Using the bibliometric method, this paper characterizes the literatures on the economic impact of climate change based on the Web of Science (WoS) Core Collection. The results reveal that the USA occupies the leading position of the studies, which publishes most documents, and contains the most productive institutes and well-known scholars. From 2009, the number of documents published by a Chinese scholar started to increase rapidly, which makes China the second most productive country in recent years. The journals both belong to the WoS Categories of economics and environmental sciences and tend to publish more literatures than others. Adaptation, vulnerability, uncertainty, economic growth, climate policy, ecosystem service, energy consumption, renewable energy, food security, and land use are the representative keywords that have both high frequency and high centrality. Potential benefits, fat-tailed risk, social cost, international migration, and sustainable intensification are the top five main research hotspots. Based on the citation network of the top 50 documents with the highest local citation score, four research trends are sorted out: (i) the methodological innovation to monetized estimate the economic impact of climate change, (ii) the effect of current and future adaptive measures on agriculture, (iii) the interactional relationship between induced technological change and carbon tax, and (iv) the effect on labor market caused by climate change. [ABSTRACT FROM AUTHOR]

The world is facing the challenges of climate change and energy structure adjustments. The role of digital finance, a new branch of business that combines digital technology and traditional financial products, in reducing global carbon emissions needs to be studied. This paper uses panel data on 280 cities in China from 2011 to 2019 to empirically examine the efficacy of digital finance for governing carbon emission reductions and the mechanism by which it does so. The results show that (1) digital finance can facilitate carbon emission reductions and help reduce carbon emission intensity within regions; (2) digital finance helps promote the rational allocation of resources and alleviates factor distortions by encouraging firms to rationally use their own factor endowments so as to reduce carbon emission intensity, which holds robustly after considering the endogenous issues such as possibly omitting variables and collinearity; and (3) differences in geographical location, the vitality of regional innovation and entrepreneurship, regional willingness to protect the environment, and environmental protection levels lead to heterogeneity in the effect of digital finance on carbon emission intensity. Therefore, it is necessary to vigorously develop digital finance as a long-term tool for carbon governance. [ABSTRACT FROM AUTHOR]

Based on the annual mean PM2.5 concentration data of 30 provinces and cities in China from 2006 to 2020, this paper constructs a structural model of manufacturing haze pollution, expounds the main mechanisms affecting haze pollution, then statistically decomposes the main factors affecting the change of pollution emissions, and examines the direct and indirect impact paths of the change of haze pollution emissions in China. The study found that (1) the inhibitory effects of latent variables on manufacturing haze pollution is from strong to weak in order: industrial structure upgrading, environmental regulation, trade opening, productivity improvement, and economic scale expansion, while the optimal path for the indirect effect of exogenous latent variables on haze pollution is to rely on environmental regulation and opening up to achieve the improvement of productivity, so as to achieve the purpose of reducing haze pollution. (2) The analysis based on the PVAR model found that industrial structure adjustment is the key factor of haze decline in both the short and long term. Environmental regulation has an obvious effect on haze control in the short term, but in the long run, it still needs to rely on industrial structure adjustment, production efficiency improvement, and trade opening to achieve the goal of reducing haze. (3) There is an inverted U-shaped nonlinear relationship between output scale and environmental regulation. In addition, trade openness has a long-term effect on productivity. Technology spillovers from opening up can reduce haze pollution in the long run by improving productivity. (4) The environmental regulation policy in the central region is characterized by "race to the bottom," while the eastern region shows the characteristic of "race to the top" in the policy game of improving productivity and optimizing the industrial structure. Therefore, haze control requires an appropriate intensity of environmental regulation to reduce the proportion of high pollution and high energy consumption industries. Make full use of the international cooperation platform of the "the Belt and Road Initiative" and the pilot free trade zone to promote substantive cooperation between Chinese enterprises and developed countries in the field of environmental protection technology, increase investment in research and development of clean equipment and cleaner production technology, improve productivity, help China's green manufacturing, and contribute Chinese wisdom and Chinese solutions to global environmental governance and climate change. [ABSTRACT FROM AUTHOR]

A comprehensive and accurate estimation of water quality in lakes and reservoirs is vital for the protection of the aquatic biota. Research on the spatiotemporal variations of nitrogen (N) and phosphorus (P) concentrations in lacustrine systems is typically plagued, however, by a lack of long‐term, spatially continuous monitoring data. This paper assembled a 30‐year (1989–2018) data set of water quality in 586 lakes and reservoirs in China, along with basin characteristics and climate conditions, forming the comprehensive data set available. These data were then used in a stacking model (based on random forest, support vector regression, and K‐nearest neighbor models) to identify the relationships between nutrient concentrations and their influencing factors, including net anthropogenic N/P inputs, geographical position, climate, land use pattern, and soil type. The stacking models were developed using data collected over multiple time scales (annual, seasonal, and monthly), which were then applied to reconstruct TN and TP concentrations during the periods of 1980–2018 and 2020s–2050s under the climate scenarios of RCP 4.5 and RCP 8.5. The accuracy of the stacking models was 99.1% and 98.3% for TN and TP concentrations using ensembled data, respectively. The interannual variations in TN and TP contents in the 586 lakes and reservoirs during 1980–2018 exhibited a non‐monotonic pattern with a peak of 1.12 and 0.049 mg/L in 2007, respectively. This study demonstrates that stacking machine learning models represent a new effective approach for estimating nutrient concentrations in unmonitored lakes and reservoirs across broad spatiotemporal scales. Plain Language Summary: The current research assembled a 30‐year (1989–2018) data set of 586 lake and reservoir water quality as well as watershed characteristics and climate conditions at a national scale to build the stacking models. As far as we know, this is the most comprehensive data set of Chinese lake and reservoir water quality. Our study first developed models take into account more indicators of the terrestrial ecosystem and consider the impact of spatial attributes and natural background conditions of lakes and reservoirs on TN and TP concentrations. We then applied the stacking model combined the results of various base machine learning algorithms to reduce the variations of the prediction and assimilating the advantages of the base models. Through containing precipitation and temperature variables with various time scales, the models have the ability to estimate and predict the annual, seasonal, and monthly variations in TN and TP concentrations during the period of 1980–2018 and 2020–2050s under different future climate scenarios. The primary target of this paper is to probe the potential benefits of using a stacking model approach to more accurately describe and reconstruct TN and TP concentrations in lakes and reservoirs throughout China over a range of spatiotemporal scales. Key Points: λ $\lambda $ This study provides spatiotemporal estimates of nutrient concentrations in 586 lakes and reservoirs of Chinaλ $\lambda $ Spatiotemporal differences of nutrient concentrations and associated drivers have been revealedλ $\lambda $ Future changes in TN and TP concentrations were estimated for selected climate scenarios [ABSTRACT FROM AUTHOR]

Based on the climate proxy data, several recent studies have concluded that the Ming dynasty's reign in China coincided with the Little Ice Age, a global crisis. In response, scholars have published several reports in recent years addressing this topic. This paper presents a comprehensive overview of the current research findings in English regarding this subject and identifies existing research gaps. The author proposes that the impact of climate on different regions during the late Ming period remains largely underexplored. Furthermore, scholars must exercise caution when assuming that adverse climatic conditions uniformly impacted the Ming empire during the Little Ice Age. This paper also highlights the use of simplistic models by scholars linking cold and dry climates to crop failure, floods, droughts, population decline, and other factors. However, any straightforward models that presume causal determination risk ignoring historical facts. [ABSTRACT FROM AUTHOR]