A modified approach of the agricultural grey water footprint considering the nitrogen fixation effect of crops in China.

The rapidly growing demand for food in human societies has led to the extensive use of fertilizers, significantly contributing to water pollution. Grey water footprints (GWF) serve as a crucial method for measuring Non-point Source (NPS) pollution, particularly in agriculture. Traditional assessments of agricultural GWF neglect biologically fixed nitrogen and the use of organic fertilizers. This research proposed a modified method to assess the GWF of Chinese agriculture from 2000 to 2020, considering the impact of Nitrogen fixation in crops and the use of organic fertilizer. We also analyzed the determinants of Agricultural Nitrogen Fixation Intensity (ANFI) using the Logarithmic Mean Divisia Index (LMDI) method to better understand factors influencing agricultural GWF. Our findings include (1) Grain cereals (e.g., maize, rice, and wheat) significantly contribute to nitrogen fixation in crop organs, accounting for 87.7%, whereas the other six economic crops contribute the rest of 12.3%. Human wastes account for Nitrogen emissions for 1.40%, and emissions by livestock product, red meat contributes 16.26%, while white meat, eggs, and milk collectively contribute 82.34%. (2) Across China, there is an overestimation of GWF by 22.4 hundred million m3 per year, about 5.13% of the total GWF measured by traditional methods. It appears that the overestimation of GWF in plain regions with more arable land tends to be somewhat more pronounced compared to plateau and coastal municipalities. Biotechnological advancements in the capacity of nitrogen fixation for key crops (e.g., maize, wheat, rice) can alleviate agricultural water pollution. The modified methodology provides a robust scientific basis for a more precise application of GWF assessments, highlighting the substantial overestimation by traditional methods in China.

Dynamic evolution and trend prediction in coupling coordination between urban and rural space utilization efficiency based local and tele-coupling model.

Optimizing the pattern of territorial space utilization is one of the key tasks to achieve the sustainable development goals. With the accelerating rate of global urbanization, the understanding of territorial space utilization efficiency, role and potential is a prerequisite for alleviating contradictions in urban and rural space distribution. The city cluster is the main form of organization for urban development in future, so the study attempted to explore the urban and rural space utilization efficiency (URSUE) in Chengdu-Chongqing urban agglomeration (CCEC) from coupling coordination degree (CCD) perspective. Considering the gradual increase in the trend of remote interactions between URSUE, we further introduced the Local and Tele-coupling coordination (LTCCD) model that takes into account interactive development relationship between different systems. The results of the study show that: In CCEC, the more economically developed cities indicated that urban spatial utilization efficiency lags behind rural spatial utilization efficiency; The LTCCD in the geographic center region will indicate a higher level but the LTCCD in the economic core cities is higher compared with their CCD level, especially in Chengdu City. This suggests that the LTCCD model is better able to take into account regional development correlations and spatial spillovers effect. This study attempts to explore several key issues of urban-rural spatial allocation in the process of urbanization development and to provide guidance for the territorial space utilization planning in urban agglomerations.

Research on the spatiotemporal evolution and influencing factors of urbanization and carbon emission efficiency coupling coordination: From the perspective of global countries.

Strategic coordination between urbanization and carbon emission efficiency (CEE) is vital for promoting low-carbon urbanization and sustainable urban planning. In order to assess the coupled coordination degree (CCD) of urbanization and CEE and investigate the factors influencing the CCD, this research employs the Super slacks-based measure (SBM) model, the coupled coordination degree model (CCDM), and the Tobit model. Four key findings emerge from the analysis of the temporal and spatial evolution traits of the CCD based on data from 106 nations worldwide between 2005 and 2020. (1) The global CEE shows a significant downward trend, and the spatial disparity is unambiguous. high CEE countries hang in the north and west of Europe, while those in Asia, Africa and the east of Europe have lower CEE. (2) The combined urbanization level and demographic, economic and social urbanization are all on an upward trend. Singapore has the greatest degree of urbanization overall globally. (3) The CCD of urbanization and CEE shows a fluctuating upward trend, with particularly strong changes in 2018-2020. 2017 and 2018 are the years with better global coupling coordination status. During the study period, the CCD results of countries are mostly uncoordinated and low coordination, and the CCD of the United States, China, India and Japan is in the front. (4) The effect of urban electrification rate on the CCD is positive; the effect of foreign trade and net inflow of foreign direct investment is negative; while energy structure and industrial structure have no significant effect. A number of policy proposals are put forth in light of the outcomes of the research to enhance the coordination.

Study on the Spatiotemporal Evolution and Driving Factors of Ecological Security in Stages Based on the DPSIRM-SBM Model: A Case Study of the Yangtze River Economic Belt.

Scientific assessment of urban ecological security (ES) is an important prerequisite to realize regional sustainable development. Previous studies lack the consideration of quality and poor systematic correlation, which could not reflect the internal dynamic relationship. On the basis of considering the time lag, this study divided the research process into the natural operation stage and the management feedback stage based on the driving forces, pressures, state, impacts, responses, management (DPSIRM) framework model and DEA theory, so as to effectively overcome the above shortcomings. Finally, we analyzed the spatio-temporal characteristics and influencing factors of the ES level of 108 cities in the Yangtze River Economic Belt (YREB) during 2005-2019. The results showed that: (a) both two stages showed a slow and fluctuating upward trend in time series, and the level of urban ES in the management feedback stage was significantly higher than that in the natural operation stage; (b) with the passage of time, the spatial distribution of ES in the natural operation stage gradually developed towards the middle and downstream of the YREB, while the management feedback stage mainly evolved from the midstream to the edge area; (c) the level of urban ES presented a different degree of spatial agglomeration phenomenon, and showed an increasing trend over time; and (d) the key influencing factors gradually changed from pressure to response during 2005-2019. This research aims to provide an innovative perspective for the measurement of urban ES, and provide scientific reference for improving urban ecological sustainable development.

How to achieve green development? A study on spatiotemporal differentiation and influence factors of green development efficiency in China.

For a long time, China 's extensive economic development model has produced a large amount of emissions, which has brought indelible damage to the environment. Green development is of vital importance for China to achieve high-quality development, and it is the core of alleviating environmental problems and promoting sustainable development. How to achieve China 's green development requires us to evaluate the level of green development in China 's provinces and analyze the reasons. In this study, an evaluation index system including undesired output of green development efficiency is constructed, and then the Supe-SBM model is used to assess the green development efficiency of 30 Chinese provinces. This paper also discusses the spatial and temporal differences as well as the factors affecting green development efficiency of green development efficiency among provinces. The findings demonstrate: (1) The green development efficiency in the eastern region is the highest, followed by the western region, while the central region has the lowest, but they all show a downward trend. (2) The spatial characteristics of green development efficiency are remarkable, according to the Global Moran's I index. However, the results of local spatial agglomeration demonstrate "small agglomeration and large dispersion," with the majority of provinces exhibiting L-L agglomeration. (3) Technological Progress, Opening Up, Urbanization Level are positively correlated with the green development efficiency. Industrial Structure, Financial Development, Energy Structure and green development efficiency are significantly negatively correlated, while Environmental Regulation shows no significant impact.

Spatiotemporal evolution and influencing factors of water resource green efficiency in the cities of the Yangtze River Economic Belt.

Effectively utilizing water resources, which is a fundamental natural resource and a vital economic resource, directly impacts how a country's economy develops. In this study, the Super-SBM model is used to calculate the city water resource green efficiency (CWRGE) of the Yangtze River Economic Belt (YREB), 108 cities that are prefecture level or higher, from 2006 to 2021. And its temporal and spatial evolution as well as its affecting variables are examined. The results indicate that, as a whole, the YREB's CWRGE has not yet achieved an effective level. The CWRGE in the YREB generally exhibits a trend of "first decreasing and then increasing, then decreasing and then increasing" and shows a "W"-shaped evolution law, and the overall trend is upward. There are just seven cities with effective data envelopment analysis (DEA), namely Changzhou, Hangzhou, Shanghai, Xuzhou, Changde, Changsha, and Yuxi. During the reporting period, the CWRGE of cities of various scales showed significant gaps: mega cities > big cities > small and medium-sized cities. From a regional perspective, the highest rate of CWRGE was found downstream of the YREB cities, then upstream, and the middle was the lowest. Spatial correlation findings demonstrated that both the agglomeration range and the outlier range were distributed, and there were mainly two positive aggregations of space forms ("high-high (H-H) type" and "low-low (L-L) type"), and the spatial distribution changed. The results of the spatiotemporal evolution demonstrate that there are more and more cities with high efficiency, as well as cities with low efficiency. From the results of the Tobit regression model, the CWRGE in the YREB are significantly improved by the economical development level, industrial scale, and water usage structure. While foreign direct investment and environmental regulation have considerable detrimental impacts, the impact of scientific and technological investment is not significant.

What were the spatiotemporal evolution characteristics and the influencing factors of urban land green use efficiency? A case study of the Yangtze River Economic Belt.

China's rapid urbanization has had a tremendous impact on the country's limited land resources, and one of the major issues of green development is how to utilize the limited land resources to maximize social, economic, and environmental advantages. From 2005 to 2019, the super epsilon-based measure model (EBM) was employed to assess the green land use efficiency of 108 prefecture-level and above cities in the Yangtze River Economic Belt (YREB), as well as investigate its spatial and temporal evolution and influential factors. The findings demonstrate that overall, urban land green use efficiency (ULGUE) in the YREB has been ineffective; in terms of city scale, megacities have the highest efficiency, followed by large cities and small and medium-sized cities; and at the regional level, downstream efficiency does have the greatest average value, followed by upstream efficiency and middle efficiency. The results of temporal and spatial evolution reveal that the number of cities with a high ULGUE is increasing in general but that their spatial characteristics are relatively dispersed. Population density, environmental regulation, industrial structure, technology input, and the intensity of urban land investment all have major beneficial effects on ULGUE, whereas urban economic development level and urban land use scale clearly have inhibitory effects. In light of the previous conclusions, some recommendations are made to continuously improve ULGUE.

Spatiotemporal pattern and multi-scenario simulation of ecological risk in mountainous cities: a case study in Chongqing, China.

Scientific descriptions and simulations of the ecological risks in mountainous areas can promote the sustainable use of land resources in these areas and improve the reliability of decision-making for ecological risk management. Taking Chongqing, China, as an example, we constructed a landscape ecological risk (LER) evaluation model based on land use data from 1995 to 2020 and analysed the spatiotemporal evolution characteristics of the LER pattern. Moreover, we coupled the patch-generating land use simulation (PLUS) model and multi-objective programming (MOP) method and input multiple scenarios (inertial development, ID; economic priority development, ED; ecological priority development, PD; and sustainable development, SD) to simulate the ecological risk pattern in 2030. The model coupling the "top-down" and "bottom-up" processes obtained optimal land use patterns in different contexts, and it was used to perform a spatially explicit examination of LER evolutionary trends in different contexts. The results showed that LER evolution in Chongqing has had obvious stage characteristics. The high-risk area decreased significantly under various constraints, including topographic, economic, and other constraints, and the distribution showed a trend of high in the west and low in the east. The LER spatial clustering characteristics were highly coupled with the risk level pattern. The ED scenario presented the most severe risk, the PD scenario presented a moderate risk, and the SD scenario balanced the land demand for economic and ecological development and had a better land use structure and LER compared with the other scenarios. The coupled model proposed in this study helps to obtain the optimal land use structure and mitigate ecological risks, thus providing a scientific basis for future urban development.

Spatiotemporal differentiation of carbon emission efficiency and influencing factors: From the perspective of 136 countries.

The severity of the global climate issue is rising, primarily as a result of excessive carbon dioxide emissions. Climate change is a global problem. How to reduce carbon dioxide emissions while promoting social and economic development is a problem that all countries need to face. This study examines global carbon emission efficiency in order to make recommendations for comprehensively improving global low-carbon development level. We extend the research scale of carbon emission efficiency from countries, regions, economic belts and sectors to the world, which can show the differences of countries and has theoretical guiding significance for global low-carbon development. This study calculates the carbon emission efficiency for 136 countries from 2000 to 2019 using the Super-EBM model. The discussion that follows examines the temporal and spatial characteristics of carbon emissions efficiency in 136 countries from the perspective of countries, developed and developing countries, and regions. Finally, the Tobit model is used to comprehensively analyze the factors that affect carbon emission efficiency. The results show that: (1) There are great differences in carbon emission efficiency among countries and regions. Only a few countries reach the production frontier, mainly in Europe, which are Switzerland, Luxembourg, Iraq, Norway, Denmark and the United Kingdom. The carbon emission efficiency of most countries is not ideal, being mainly concentrated in Asia and Africa, and has not achieved significant improvement over time. Asia has the lowest carbon emission efficiency. Mongolia, Ukraine, Iran, Angola, Belarus and Uzbekistan are the key governance areas for global energy conservation and carbon emissions reduction. (2) Developed countries have the much higher average carbon emission efficiency than developing countries. Combined with the industrial development stages of developed and developing countries, this is in line with the environmental Kuznets curve (EKC) hypothesis. The average carbon emission efficiency gap between developing and developed countries shows a trend of "first narrowing and then widening", which demonstrates that developing countries' reliance on energy input to boost their economies will improve carbon emission efficiency, but only temporarily. (3) Urbanization level, foreign trade and proportion of renewable energy effectively improve the carbon emission efficiency, while industrial structure and proportion of electricity users have an inhibitory effect on the carbon emission efficiency. Global low-carbon development should be hastened by strengthening international cooperation, optimizing industrial structure, promoting urbanization and foreign trade, and adjusting the energy structure.

Carbon deficit checks in high resolution and compensation under regional inequity.

Carbon compensation is an effective way of reducing carbon emissions. However, previous studies in this field have been limited and have not examined high-precision scientific carbon compensation under regional inequity. The present study examined initial carbon compensation in the grid and developed a new equitable carbon compensation model. Additionally, it modified the carbon compensation value for each province and analysed how land-use change affected carbon compensation. The results show that, after the modification, the entire carbon deficit reached 17.34 × 108 t C in 2015, representing a decrease of 14% compared with the initial carbon deficit. The area with negative carbon deficit values accounted for 36% of the whole area, concentrated mainly in the south, southwest and northwest. Without modification, the initial carbon compensation reached 537 × 108 USD, and only Yunnan, Sichuan and Hainan provinces being eligible to receive compensation. The final modified carbon compensation was approximately 20% of the initial values, and 11 provinces were eligible to obtain compensation. The other provinces responsible for paying the carbon compensation costs were typically concentrated in Central and Eastern China. Land-use changes in 2015 led to increases in the initial carbon compensation and modified carbon compensation of 3.74 × 108 and 0.13 × 108 USD, respectively. The per-unit land-use change caused greater increases in carbon emissions in China's big cities and the provinces in Central and East China. Some policies, such as macro-control by the central government, diversified forms and patterns of compensation, and auxiliary measures should be formulated/proposed.

Temporal and spatial changes and influencing factors of low-carbon economy efficiency in China.

Low-carbon development has always been an important focus of China's economic transformation. In order to promote the development of low-carbon economy, this study used SBM-DEA model to evaluate China's provincial LCEE from 2005 to 2019, studied its temporal and spatial evolution law, used spatial autocorrelation to explore the correlation of China's provincial LCEE, and explored the key influencing factors of LCEE with Tobit model. The empirical results show that the LCEE of most provinces in China is declining, and there are significant differences among different regions in China. Because the eastern region of China can rely on its own human resources, capital environment, and economic foundation, the overall LCEE level is relatively high, while the central and western regions still have obvious deficiencies due to industrial conditions, geographical location, and other factors. LCEE has significant spatial correlation, and neighboring provinces have spillover effects on local LCEE. On this basis, the key factors that affect LCEE are determined. Urbanization level, traffic level, economic development level, financial development, investment in fixed assets, and energy consumption are the important factors that affect LCEE in China, but these influences vary from province to region. It is more reasonable for local governments to develop low-carbon economy according to their own conditions.

Green Total-Factor Energy Efficiency of Construction Industry and Its Driving Factors: Spatial-Temporal Heterogeneity of Yangtze River Economic Belt in China.

With the proposal of the "carbon peak, carbon neutral" goal, energy efficiency has become one of the key means to achieve energy conservation and emission reduction at this stage. The construction industry, as a cornerstone of China's economy, is characterized by serious overcapacity, energy waste, and pollution. As a result, academic research on its energy efficiency is gaining traction. This paper employed the Super-EBM model considering undesirable output to evaluate the green total-factor energy efficiency of the construction industry (CIGTFEE) in the Yangtze River Economic Belt (YREB) from 2003 to 2018. The spatial-temporal evolution characteristics and spatial heterogeneity of CIGTFEE were analyzed in detail through geospatial analysis. Finally, the driving factors of CIGTFEE were analyzed through a spatial econometric model. The results indicated that, during the sample research period, the CIGTFEE showed a holistic growth trend with volatility. By region, the downstream CIGTFEE grew sharply until 2006 and then remained fairly stable, while the midstream conformed to the "M" trend and the upstream region showed an inverted u-shaped trend; From the perspective of spatial differentiation, the CIGTFEE in YREB shows a significant spatial agglomeration situation, while the spatial agglomeration degree weakened. It existed a ladder-shaped change trend, with the regional average CIGTFEE from high to low levels as follows: Downstream, Midstream, and Upstream, and showed an obvious polarization in the upstream and downstream. From the analysis of the driving factors, CIGTFEE is significantly promoted by economic growth, energy structure, and human capital and suppressed by urbanization level, yet the impact of technological progress and the level of technology and equipment is not significant. In summary, province-specific policies based on spatial and temporal heterogeneity were proposed to improve the CIGTFEE of YREB.

Analysis of the spatial distribution and influencing factors of China national forest villages.

China national forest villages are the agents to promote rural greening and beautification, as well as further implementation of the rural revitalization strategy. It is of great significance to study their spatial distribution characteristics and influencing factors. Therefore, taking 7586 China national forest villages as examples, the methods of nearest neighbor index, Tyson polygon, cold and hot spot analysis, and nuclear density index are used to study the spatial distribution characteristics of China national forest villages and their influencing factors. The results show that (1) since the nearest neighbor index is less than 1, and the Tyson polygon area variation coefficient is much greater than 64%, it is comprehensively determined that the distribution of China national forest villages belongs to agglomerated distribution. (2) The spatial clustering is characterized by "hot in the south and cold in the north." The hot spots are dominated by southern regions such as Sichuan, Hubei, and Jiangsu, and the cold spots are dominated by northern regions such as Heilongjiang, Jilin and Xinjiang. (3) The distribution characteristics of nuclear density have a strong correlation with the distribution characteristics of forest vegetation and urban agglomerations. Most high-density areas are located within the forest vegetation coverage. The first batch forms the Yangtze River Delta and Central Plains urban agglomerations high-density areas, and the second batch forms the Yangtze River Delta and Central Plains high-density areas. (4) Elevation, aspect, river, forest resources endowment, traffic, economic development level, and population size are important factors affecting the distribution of China national forest villages, and their distribution presents the characteristics of "low altitude, positive direction, near water, rich forest resources, convenient transportation, developed economy, and dense population." The research can provide reference for the evaluation and construction of China national forest villages and the implementation of village beautification and rural revitalization strategies in the future.

China's industrial green total-factor energy efficiency and its influencing factors: a spatial econometric analysis.

The sustainable development of China's economy is bottlenecked by resource shortage and environmental pollution. As the leading resource consumer and pollutant source, the industrial sector needs to improve its energy efficiency. This paper establishes a super epsilon-based measure (Super-EBM) model with bad outputs like environmental cost and evaluates the industrial green total-factor energy efficiencies (IGTFEEs) of 30 provinces in China during 2000-2017. Unlike previous research, the main contribution of this paper is to choose four environmental pollutants as bad outputs (industrial carbon dioxide, industrial sulfur dioxide, industrial chemical oxygen demand, industrial solid waste). By contrast, the previous studies mostly only take one environmental pollutant as bad output, i.e., the bad outputs are not fully measured. Then, the spatiotemporal dynamics and spatial correlations of the IGTFEEs were analyzed, and the influencing factors of IGTFEE were examined empirically with a spatial econometric model. Finally, this paper adopts generalized method of moments (GMM) to solve the endogenous problem, trying to assure the robustness of estimation results. The results show significant provincial differences in IGTFEE. Most eastern coastal provinces achieved satisfactory IGTFEEs, while most inland provinces had undesirable IGTFEEs. Eastern region achieved the highest IGTFEE, followed by central region; western region had the lowest IGTFEE. The IGTFEE improved over time in some provinces while worsened greatly in some provinces. The IGTFEE in most provinces need to be further improved. Global Moran's I values indicate that the provincial IGTFEEs were clustered in space, rather than randomly distributed. Local indication of spatial association (LISA) map reflects significant local spatial clustering of provincial IGTFEEs. In addition, IGTFEE is significantly promoted by industrial structure, technological innovation, human capital, opening-up, and energy structure yet significantly suppressed by ownership structure and environmental regulation. Considering the endogeneity, GMM results show that the estimation results of the model were robust. Specific policy recommendations include vigorously developing high-tech industries, deepening state-owned enterprises reform, diverting more funds to research and development, cultivating versatile talents, introducing environmentally-friendly foreign capital, accelerating the implementation of clean energy development strategy, and widening the fund channels of pollution control investment.

Spatiotemporal changes in efficiency and influencing factors of China's industrial carbon emissions.

The industrial sector is the backbone of China's national economy. The industrial carbon emission efficiency (ICEE) of China is directly related to the achievement of carbon emission reduction targets. This paper reports on the use of the minimum distance (min-SBM) method to determine the ICEE of 30 provinces in China during 1998-2015, as well as the use of a spatial econometric method to investigate the convergence and influencing factors of the regional ICEE. The results indicate significant regional differences in the ICEE. The provinces with higher average values of ICEE are located in the eastern coastal areas, whereas the provinces with lower average values of ICEE are located in the central and western inland regions. The results of the spatial autocorrelation index reveal that China's inter-provincial ICEE exhibits significant spatial autocorrelation characteristics, and its spatial distribution demonstrates a certain regularity. The local indicators of spatial association diagram further illustrate that most provinces in China have high and low agglomeration values. With the introduction of the spatial effect, the absolute and conditional convergence rates increase. In addition to the non-significant industrial structure effect, the level of economic development, foreign direct investment, technological progress, and government intervention demonstrate a positive impact on the ICEE convergence, whereas the energy consumption structure has a negative impact. This work investigates the cause for the regional gap in China's current ICEE. Suggestions for improving the efficiency of China's industrial carbon emissions and narrowing the regional gap are provided, which serve as a reference value for China to achieve the peak of carbon dioxide emissions before 2030.

Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China).

To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments.