Long-term effects of massive building material use in China, which experienced intense urbanization in the past two decades, remain insufficiently explored. Here, to fill these gaps, we developed a high-resolution time-series database of building material stocks from 2000 to 2019 and found that China held 15

Reducing carbon emissions in China and “Belt and Road” countries (BCEs) is crucial to curbing global warming. This study examines carbon emission dynamics in 134 China and “Belt and Road” countries (CABs) using social network analysis (SNA) to characterize network structure and an exponential random graph model (ERGM) to identify the factors influencing network formation. The results are as follows: (1) The status of countries in the BCEs network is becoming more equal. (2) The correlation of BCEs is increasing, indicating significant potential for further development. The spatial correlation network of BCEs exhibits small-world characteristics, which are gradually weakening. (3) The synergistic capacity of BCEs is influenced by bidirectional reciprocal relationships, national attributes, and external environments. This study provides a comprehensive assessment of the synergistic carbon emission reduction capacity of CABs and discusses the factors affecting this capacity. It offers valuable insights for formulating differentiated and synergistic carbon emission reduction policies. SNA is used to explore spatial complexity of carbon emission from 2002 to 2022. Countries’ status in carbon emission networks is becoming increasingly equalized. Correlation of carbon emissions among countries is growing. Carbon emissions networks have the small-world characteristic. ERGM is used to reveal the influencing factors of carbon emission.

Abstract Globally, the rising frequency and severity of droughts, tropical cyclones, floods, and heatwaves are inflicting significant economic damage. While existing research has primarily concentrated on the impacts of climate risks on developed countries, emerging economies with sizable market value, which are more vulnerable to the effects of climate change, have received little attention. An event study and stacked difference-in-differences approaches are employed to estimate the effects of floods on the housing market within an emerging economy, with a specific focus on China. On average, the occurrence of floods leads to a 2.8% decrease in housing prices, while it surprisingly increases the transaction quantity by 36.6%. Despite the negative impact on property values, the volume of housing transactions remains brisk, possibly due to the attraction of lower prices for potential buyers. The severity of flood risk, financial aid allocation, and the disclosure of flood-prone information play significant roles in shaping the market’s response to flood risk. Mechanism analysis shows that the market response to floods is influenced by public risk perception and short-term physical damage caused by floods. In areas with a history of frequent floods, the housing market appears to adjust more rationally, with buyers incorporating flood risk into their purchase decisions. Conversely, in regions where floods are less frequent, the market response tends to be more pronounced, with significant price discounts post-flood events. Short-term physical damage also affects housing prices by reducing prices as buyers care about property quality. Flood-prone information disclosure helps to mitigate information asymmetry in the housing market. The disclosure of such information is found to reduce the information gap, leading to a higher price discount on flooded houses and a corresponding increase in transaction volume. In conclusion, climate change-related risks such as floods significantly affect the asset market of emerging economies like China, which are priced in the housing market. Understanding and incorporating climate risks into housing market analysis and policymaking is crucial, particularly amid a backdrop of escalating climate-related disasters.

Proximity to higher education institutions has played an important role in determining land prices. However, little is known about their effects due to data limitations. We take advantage of the mushrooming of new satellite campuses of universities since China's 1999 higher education expansion to examine the impact of higher education institutions on urban land prices using micro data on land parcel transactions. Using a two-way fixed effects estimator, we find that new satellite campuses increase land prices by 6-13 percent in host counties with greater effects on commercial & residential land. These results survive several robustness checks and heterogeneity-robust estimators. Heterogeneity analysis reveals that new satellite campuses built by four-year institutions have a larger impact on the price of commercial and residential land. The impact on industrial land is more prominent in less developed regions. These results have important implications by bringing to light a key factor in shaping land prices and contributing to understanding the role of higher education institutions in China's rapid urbanization.

Classifying household water-consumption behaviors is crucial for providing targeted suggestions for watersaving behaviors and enabling effective resource management and conservation. Although it is common knowledge that energy consumption is closely coupled with household water consumption, the effectiveness of energy consumption information in classifying household water-consumption behaviors remains unexplored. This study proposes a hybrid model of long short-term memory (LSTM) and random forest (RF) using water and electricity consumption as inputs to classify household water-consumption behaviors. Data from three households in Beijing collected from January to March 2020 were used for the case studies. The hybrid model achieved a macro F1 score of 0.89 at a 5-min resolution, outperforming the standalone LSTM and RF models. Additionally, the inclusivity of time-series electricity consumption improves the accuracy (F1 scores) of classifying bathing and laundry behaviors by 0.12 and 0.20, respectively. These findings underscore the scientific value of integrating electricity consumption as a proxy variable in water-consumption behavior classification models, demonstrating its potential to enhance accuracy while simplifying data acquisition processes. This study establishes a framework for demand-side water management aimed at empowering residents to understand their own water-energy consumption behavior patterns and engage in personalized water conservation efforts.

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model's ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability.

This study explores the impact of wind facilities on land values based on wind farm construction and land transaction datasets in China from 2005 to 2017. We implement a two-way fixed effects model to estimate the causal effects of the siting of wind farms on land prices. Our results show that the siting of wind farms significantly impacts land transaction prices. On average, land parcels located within 10 km of a wind farm enjoy a 4.78% price premium. However, land parcels located within 1 to 3 km of a wind farm experience depreciation, while lands located within 3 to 6 km of a wind turbine experience an increase on average. We further find that offshore wind farms are viewed more favorably by nearby residents compared to inland wind farms.

Cumulative effects assessment (CEA) should be conducted at ecologically meaningful scales such as large marine ecosystems to halt further ocean degradation caused by anthropogenic pressures and facilitate ecosystem-based management such as transboundary marine spatial planning (MSP). However, few studies exist at large marine ecosystems scale, especially in the West Pacific seas, where countries have different MSP processes yet transboundary cooperation is paramount. Thus, a step-wise CEA would be informative to help bordering countries set a common goal. Building on the risk-based CEA framework, we decomposed CEA into risk identification and spatially-explicit risk analysis and applied it to the Yellow Sea Large Marine Ecosystem (YSLME), aiming to understand the most influential cause-effect pathways and risk distribution pattern. The results showed that (1) seven human activities including port, mariculture, fishing, industry and urban development, shipping, energy, and coastal defence, and three pressures including physical loss of seabed, input of hazardous substances, nitrogen, and phosphorus enrichment were the leading causes of environmental problems in the YSLME; (2) benthic organisms, fishes, algae, tidal flats, seabirds, and marine mammals were the most vulnerable ecosystem components on which cumulative effects acted; (3) areas with relatively high risk mainly concentrated on nearshore zones, especially Shandong, Liaoning, and northern Jiangsu, while coastal bays of South Korea also witnessed high risk; (4) certain risks could be observed in the transboundary area, of which the causes were the pervasive fishing, shipping, and sinking of pollutants in this area due to the cyclonic circulation and fine-grained sediments. In future transboundary cooperation on MSP, risk criteria and evaluation of existing management measures should be incorporated to determine whether the identified risk has exceeded the acceptable level and identify the next step of cooperation. Our study presents an example of CEA at large marine ecosystems scale and provides a reference to other large marine ecosystems in the West Pacific and elsewhere.

The global trade networks of maize, wheat, and rice and their carbon emission networks are established from 2000 to 2020. Social network analysis (SNA) is used to quantify countries' roles in the networks. Spatial econometric model is used to analyze the impacts of socio-economic indicators and the countries' roles according to Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. The results show that the global cereal trade volume and their carbon emissions continues to rise. The overall cereal trade networks tend to be complicated and tight. Global cereal trade networks are highly heterogeneous networks with numerous peripheral economies and a few hub countries. However, with the rise of developing economies in cereal trade, the dominance of hubs decrease. The panel regression shows that the network characteristics of three cereal trade have different effects on carbon emissions as follows: (1) In rice trade network closeness centrality increases carbon emissions, meaning countries or regions with more trade connectivity emit more carbon emission. However, in wheat trade network it has reducing effects in whole and low-income countries, while increasing effects in high-income countries. (2) Among the three cereal trade, betweenness centrality reflecting mediating role has significantly negative effects on carbon emissions only in rice trade network. (3) Regarding the number of importing partners, it has positive effects on carbon emissions for wheat and maize trade, while negative for rice trade. The number of exporting partners has positive effects in all the three cereal trade. This study confirms carbon mitigation potentials from trade relationship. This study also discusses some implications for agriculture and policymakers in different countries or regions involved in the global cereal trade.

Urban heat island (UHI) has a great influence on human wellbeing in urban areas. Previous studies have investigated impact on environment, ecosystem, and human health of UHI. To investigate economic impact of UHI is a lack of research. In this study, spatial-econometric hedonic housing price models are developed for estimating the marginal value of summer UHI in Beijing, China. The results showed that UHI effects in Beijing became severe from 2015 to 2017. The heat islands showed a mix of high and sub-high land surface temperature (LST). The spatial Durbin model estimates that one-unit increase of UHI intensity (UHII) would lower the housing value by 3.91%. Regarding the different districts, households had different attitudes to the UHI effect. Specifically, households regarded UHI effect as amenity in the four suburban districts with hills and relatively low LSTs; however, in most urban districts and their surroundings, households were willing to pay to avoid UHI effects with 13.0 dollars to 826.3 dollars for one-degree UHII decrease. This study highlights the urgent need for planners and decision-makers to consider urban expansion, UHI effect, and their economic impact in future urban planning.

Carbon emissions based on land use change have attracted extensive attention from scholars, but the current land use carbon emission accounting model is still relatively rough. Despite the continuous promotion of China's ecological civilization strategy, whether green economic development promotes carbon emission reduction remains to be studied. This study uses the Exploratory Spatial-temporal Data Analysis (ESTDA) framework system to revise the land-use carbon emission accounting model; it integrates the NDVI adjustment index and systematically analyzes the spatial and temporal patterns and evolutionary path characteristics of carbon emissions from 2000 to 2020 for 130 prefecture-level cities in the eastern coastal region of China, a high carbon emission region. The spatial econometric model is further used to explore the impact of green economy development on carbon emissions. The results show that the spatial distribution of carbon sources and sinks in the eastern coastal cities demonstrates a year-on-year increase during the study period. The spatial distribution of carbon sources is higher in the north than in the south, and the economically developed regions are more elevated than less developed economic areas. Net carbon emissions show prominent spatial clustering characteristics. The south has a more stable internal spatial structure than the north, and the inland has a more stable internal spatial structure than the coast. Green economic development can significantly reduce carbon emission intensity and has a significant spatial spillover effect. The findings imply that policy-makers need to consider the spatial and temporal distribution and spatial correlation of carbon emissions among cities; they can achieve carbon emission reduction by formulating a more reasonable green economy development approach and implementing regional linkages.

Water, energy and food are three essential resources for the socio-economic system, and they are interlinked. The coordination of their internal relations is worth studying. We conduct a coordination evaluation method to assess the water-energy-food nexus (WEF Nexus) in China's provinces. By combining the coupling model and the coupling coordination model, we measure the comprehensive evaluation index and coupling coordination degree of China's 30 provinces from 2005 to 2017. First, the results show the provincial comprehensive evaluation index had a slow upward trend. The comprehensive evaluation index of the southern region was higher than that of the north, and the eastern was higher than the west. Second, the coordination degree of WEF Nexus in China's 30 provinces has reached high level in the horizontal coupling stage, and the overall degree of coupling coordination was on the rise. In 2017, the WEF Nexus coupling coordination degree of most provinces reached 0.700 or more, which was intermediate-coordinated. In the six years, the 30 provinces have experienced five types of coupling coordination degree: near coordinated, barely coordinated, primary coordinated, intermediate-coordinated, and well-coordinated.

Given the growing awareness of sustainable development, the environmental protection industry has attracted much attention. Green finance has developed rapidly in policymaking and practices. This study provides a framework for evaluating green finance via linkage analysis based on input–output theory. Measurements on industrial linkages are calculated in China in two provinces from 2002 to 2018, which study the relationship between finance and environmental protection sectors. The results show that the environmental protection sector (EPS) in China has gradually developed from a sector with weak backward and strong forward linkages to a sector with strong backward and weak forward linkages from 2002 to 2015; however, in 2017 and 2018, the EPS returned to a sector with weak backward and strong forward linkages. At the provincial level, the EPS used to be a key sector with strong backward and forward linkages. The connection between the finance sector and the EPS rose first, then declined in the country and the Zhejiang province; Guangdong had a similar evolution in the former period, but it had a rising trend in the latest year. The findings provide insights for further promoting the support from the finance sector to the environmental protection activities.

As the largest holder of shale gas resource estimates, China is actively promoting its shale gas development to steer its transitions to a low carbon energy system. The production of shale gas usually needs a large amount of water. According to our estimates, the direct water consumption is about 9700-37600m(3)/well, and the indirect water consumption is around 32,400-71,100 m(3)/well. Such a large amount of water consumption could have a serious impact on local human and ecosystem water consumption since China is a country with scarce and unevenly distributed water resources. Water scarcity footprint (WSF) of shale gas production in Chinese provinces is assessed to understand the impacts of shale gas production on local water consumption for other sectors. The results show that the average water pressure for shale gas production in China is higher compared with that of the U.S.. The average WSF in China is 16,574 m(3) (world . eq)/10(6) m(3) gas while the WSF in the Barnett shale region in the U.S. is only around 2000 m(3) (world. e q)/10(6) m(3) gas. 13 of 31 provinces have even higher WSF than the national average, in which the amount of shale gas resources accounts for about 20% of China's total. Shale gas exploitation in these 13 provinces might not be suitable or must be cautious from the perspective of WSF. The remaining 18 provinces have lower WSFs than the national average. A sustainable way for extracting shale gas in these 18 provinces needs to comprehensively consider WSF, the scale and speed of exploitation and the amount of local shale gas recoverable reserves.

This paper uses Bayesian methods to estimate the European (Monetary) Union effect on trade. The high dimensionality of the parameter space when estimating gravity equations with many dummy variables results in standard hypothesis tests with a large Type I (false positive) error. Bayesian methods are able to handle this problem; they also provide a principled method of model selection that can be applied to different specifications of the dummy variables. Bayesian model selection tests prefer our most unrestricted dummy specification, which includes asymmetric bilateral effects, as well as time-varying, country-specific factors. Our estimate shows a zero Euro effect on trade, but a 14.8% increase in imports for a member of the European Union during 1980-2004.

China has suffered from severe crop residue burning (CRB) for a long time. As a type of biomass burning, CRB leads to a huge alteration in climate due to the emission of greenhouse gases and particulates in the atmosphere and damages to surface characteristics on land. At present, a growing body of research focuses on the impact of biomass burning (BB) (e.g., forest fire, grass fire, and CRB) on climate change from the aspect of atmospheric process. Meanwhile, a small number of research studies have started to pay attention on the damage caused by BB (e.g. forest fire) on land surface and consequent changes in the land surface temperature (LST). However, at present there is no study concerning the effect of CRB on the surface temperature. Considering its large incidence, highly seasonal concentration, and large spatial scale in China, this study attempted to reveal the impact of CRB on LST. Specifically, we identified the influence of CRB on the LST in surrounding areas based on MODIS Thermal Anomalies/Fire product and MODIS LST product during the CRB season for three provinces of China: Heilongjiang, Hebei, and Guangxi from 2015 to 2017. The results showed that there was a strong positive correlation between daily CRB spots and daily LST (R between 0.30 and 0.61) in Heilongjiang (20,184-24,902 spots of CRB from 2015 to 2017). On the other hand, in Hebei and Guangxi provinces, where the total CRB number was less (2,367-2,754 spots of CRB from 2015 to 2017 in Hebei, and 701 to 653 spots of CRB from 2015 to 2017 in Guangxi), even if R was only 0.36-0.53 and 0.11-0.53, respectively, the peaks of CRB spots and peaks of daily LST were highly matched. Furthermore, the spatial analysis showed that LST on agricultural land in 10-15 km distance from the center of CRB was higher (1-3 degrees C) than that in other regions in Heilongjiang province. On the other hand, the influence scale of CRB in Hebei and Guangxi was only 2-4 km with a similar to 2 degrees C increase on LST. Finally, according to the typical case analysis, it was found that the influence of CRB on LST existed for 1-3 days and did not disappear immediately. The study proved the impact of CRB on LST in surrounding areas, and favors action on climate change relief through CRB control. (C) 2020 Elsevier Ltd. All rights reserved.

Combating global climate change calls for all countries to accelerate emissions reductions, and equity is an important guiding principle. A new indicator – the carbon Palma ratio, extended from the income Palma ratio and defined as the ratio of the total emissions of the top 10% emitters to those of the bottom 40% – is proposed, which provides a new perspective to inform the international community and the public of the distribution inequality of carbon emissions among individuals. The ratio is quantified within and across countries, by applying an elastic relationship between individual emissions and income. Results show that the carbon Palma ratios within most developing countries are overall high, suggesting them to focus more on coordinating regional and income differences and mainly guide high emitters to mitigate, so as to improve emissions and income equity simultaneously. The carbon Palma ratios within developed countries are comparatively smaller; however, the greater historical responsibilities to warming suggest them to substantially reduce emissions of all citizens, so as to enhance national mitigation contributions systematically. At a global scope, the current carbon Palma ratio is observably higher than within any country, reflecting an extremely severe inequality when looking at individual emissions beyond territorial limitations. The regional decomposition of emitters further suggests developed countries to take the lead in the post-Paris era in ratcheting up mitigation and climate finance ambition.

Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R2) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050.

Urban green vegetation provides amenity value for urban residents and improves the living environment. These changes in value can be reflected in the variation of house prices. This paper uses a unique housing transaction data set to estimate the impact of urban green vegetation on house values in Beijing, China, using the hedonic price model. To measure urban green vegetation, we calculated the normalized difference vegetation index (NDVI) by time-series Landsat TM 8 remote-sensing imagery. For model specification, we applied spatial lag models to address spatial spillover effects that might lead to biased estimates if ignored. Spatial hedonic results indicate that urban green vegetation has a positive effect on residential housing prices when the measurement scale of NDVI value is smaller than or equal to 18.01 acres for each block of flats, whereas greater NDVI values generate a negative effect on property values if the scale is larger. This implies that urban green vegetation within 135m could increase house prices in the range of 7.95-10.59%, which indicates a potential 20.2-26.9billion yuan increase in the value of the sample real estate market in Beijing.

Biomass is a crucial option of substituting fossil fuels to reduce emissions, and bioenergy with carbon capture and storage (BECCS) allows for obtaining net-negative emissions. We explore the role of biomass in China's long-term mitigation toward the Paris climate goals in light of three narratives and five mitigation scenarios, modeling by a refined Global Change Assessment Model. While presenting a limited contribution to achieving China's Nationally Determined Contribution (NDC), biomass plays an important role in China's post-NDC mitigation toward the Paris climate goals. All the assessed scenarios call for extensive biomass use, accounting for 6.5%–28% of China's 2100 primary energy in our three 2 °C scenarios and 15%–30% in our two 1.5 °C scenarios. The exact biomass deployment trajectories tend to depend greatly on how China envisages national mitigation paces and BECCS strategies. For either 2 °C or 1.5 °C, a smaller negative-emission narrative, which means a more rapid immediate decarbonization of the energy system toward mid-century, depends on larger bioenergy in medium-to-long-term. Delaying short- and medium-term ambition delays bioenergy applications but requires far more in the second half of the century to create greater negative emissions via BECCS. Moving from 2 °C toward 1.5 °C features higher and earlier bioenergy deployments and meaningfully increasing BECCS volumes and biofuel shares in China's energy system. Consequently, the Chinese stockholders might be ready to make a decision on to what degree biomass and BECCS enter the sphere of China's energy and climate policies, which will greatly influence not only national biomass roadmap but also mid-century mitigation targets.