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Land cover is being continuously transformed at an accelerating pace because of urbanization and economic development, which is, in turn, impacting ecosystem services and human well-being. Consequently, there is a need to enhance sustainable land use management to achieve high levels of land eco-efficiency across different regions in China. Accomplishing this entails adjustments not only in terms of the spatial layout of land but also in land use management. The relationship between land use management and land eco-efficiency was explored taking Hebei, a province of China, as a case study. With the help of Stochastic Frontier Analysis (SFA) and other statistical analysis, we analyzed land use conversions and land eco-efficiency in Hebei, China. In this study, we first explored the relationship between land use conversion, ratio of cultivated land, and crop production using scatter plots. Further, we analyzed the land eco-efficiency and ecological performance of cities in Hebei based on SFA. The findings of the study revealed that land use output is the key factor linking land use management and land eco-efficiency. Spatial differences of land eco-efficiency are clearly apparent in Hebei, and the results of the study showed a corresponding decrease in land eco-efficiency with a reduction in the distance to the city center. In conclusion, a step-by-step regulatory process for improving land eco-efficiency within China’s land use management scheme is proposed.

Ecosystem services are substantial elements for human society. The central challenge to meet the human needs from ecosystems while sustain the Earth＇s life support systems makes it urgent to enhance efficient natural resource management for sustainable ecological and socioeconomic development. Trade-off analysis of ecosystem services can help to identify optimal decision points to balance the costs and benefits of the diverse human uses of ecosystems. In this sense, the aim of this paper is to provide key insights into ecosystem services trade-off analysis at different scales from a land use perspective, by comprehensively reviewing the trade-offs analysis tools and approaches that addressed in ecology, economics and other fields. The review will significantly contribute to future research on trade-off analysis to avoid inferior management options and offer a win-win solution based on comprehensive and efficient planning for interacting multiple ecosystem services.

The question of how to generate maximum socio-economic benefits while at the same time minimizing input from urban land resources lies at the core of regional ecological civilization construction. We apply stochastic frontier analysis (SFA) in this study to municipal input-output data for the period between 2005 and 2014 to evaluate the urbanization efficiency of 110 cities within the Yangtze River Economic Belt (YREB) and then further assess the spatial association characteristics of these values. The results of this study initially reveal that the urbanization efficiency of the YREB increased from 0.34 to 0.53 between 2005 and 2014, a significant growth at a cumulative rate of 54.07%. Data show that the efficiency growth rate of cities within the upper reaches of the Yangtze River has been faster than that of their counterparts in the middle and lower reaches, and that there is also a great deal of additional potential for growth in urbanization efficiency across the whole area. Secondly, results show that urbanization efficiency conforms to a “bar-like” distribution across the whole area, gradually decreasing from the east to the west. This trend highlights great intra-provincial differences, but also striking inter-provincial variation within the upper, middle, and lower reaches of the Yangtze River. The total urbanization efficiency of cities within the lower reaches of the river has been the highest, followed successively by those within the middle and upper reaches. Finally, values for Moran’s I within this area remained higher than zero over the study period and have increased annually; this result indicates a positive spatial correlation between the urbanization efficiency of cities and annual increments in agglomeration level. Our use of the local indicators of spatial association (LISA) statistic has enabled us to quantify characteristics of “small agglomeration and large dispersion”. Thus, “high- high” (H-H) agglomeration areas can be seen to have spread outwards from around Zhejiang Province and the city of Shanghai, while areas characterized by “low-low” (L-L) patterns are mainly concentrated in the north of Anhui Province and in Sichuan Province. The framework and results of this research are of considerable significance to our understanding of both land use sustainability and balanced development.

With 80% of the world's carbon emissions coming from urban areas and most part of the world still experiencing ever accelerated process of urbanization, China faces huge pressure to achieve the carbon emission peaking in 2030 and realizes the goal of carbon neutrality before 2050. Therefore, this study explored the spatial variability of CO 2 emissions from urban construction land among 30 provinces in China, analyzed its driving factors and estimated their potentials for emission reductions from 2000 to 2018. The results demonstrate that: (1) according to the IPCC model, both the carbon emission amounts and carbon emission intensity from urban construction land showed an upward trend from 2000 to 2018. (2) Decomposition analysis of logarithmic mean Divisia index revealed that economic level has positive impact on carbon emissions. Energy efficiency and energy structure are the negative contribution factors to the carbon emissions, and the energy efficiency effect played a more important inhabiting factor. (3) The carbon emission reduction potential indexes was provided to estimate the carbon emission reduction potential of 30 provinces in China; it indicated that 17 provinces have their carbon emission reduction potential indexes less than 1, and they confront with mandatory push to reduce carbon emission under the current national policy. Finally, promoting clean energy and applying internet of things into energy transport corridor system and more low-carbon land planning policies are suggested to facilitate more effective implementation of carbon emissions reduction actions in China.

Increases in atmospheric carbon dioxide (CO 2 ) concentrations is the main driver of global warming due to fossil fuel combustion. Satellite observations provide continuous global CO 2 retrieval products, that reveal the nonuniform distributions of atmospheric CO 2 concentrations. However, climate simulation studies are almost based on a globally uniform mean or latitudinally resolved CO 2 concentrations assumption. In this study, we reconstructed the historical global monthly distributions of atmospheric CO 2 concentrations with 1° resolution from 1850 to 2013 which are based on the historical monthly and latitudinally resolved CO 2 concentrations accounting longitudinal features retrieved from fossil-fuel CO 2 emissions from Carbon Dioxide Information Analysis Center. And the spatial distributions of nonuniform CO 2 under Shared Socio-economic Pathways and Representative Concentration Pathways scenarios were generated based on the spatial, seasonal and interannual scales of the current CO 2 concentrations from 2015 to 2150. Including the heterogenous CO 2 distributions could enhance the realism of global climate modeling, to better anticipate the potential socio-economic implications, adaptation practices, and mitigation of climate change. Measurement(s) atmospheric carbon dioxide concentrations Technology Type(s) satellite observations • spatial statistical analysis

Since the implementation of market oriented economic reform in 1978, China has been on the track of rapid urbanization. The unprecedented urbanization in China has resulted in substantial cultivated land loss and rapid expansion of urban areas. The cultivated land loss due to urbanization not only threatens food security in China, but has also led to ecological system degradation to which close attention should be paid. Therefore, we examined the effects of the conversion from cultivated to urban areas on the ecosystem service in the North China Plain on the basis of a net primary productivity based ecosystem service model (NESM) and a buffer comparison method. Cultivated land loss due to urbanization in the North China Plain led to a total loss of ecosystem service value of 34.66% during the period 1988–2008. Urban expansion significantly decreased the ecosystem service function of water conservation (–124.03%), nutrient cycling (–31.91%), gas regulation (−7.18%), and organic production (–7.18%), while it improved the soil conservation function (2.40%). Land use change accounted for 57.40% of the changes in ecosystem service and had a major influence on the changes in nutrient cycling and water conservation. However, climate change mainly determined the changes in gas regulation, organic production, and soil conservation.

Decades of reckless deforestation have caused serious soil erosion and land desertification issues in the Loess Plateau (LP). “Grain for Green” Program (GFGP), one of the world’s largest ecological restoration projects, is crucial to improve the ecological environment. Previous studies have demonstrated that GFGP lowers soil erosion in the LP. However, there are trade-offs and synergies between ecological services. Does strengthening soil conservation prevent enhancing other ecosystem services? Consequently, can the GFGP improve many ecological services simultaneously? This study compares changes in NDVI prior to and following the implementation of the GFGP in LP to the enhancement of ecosystem services. During the research period, the LP’s overall vegetation cover rose significantly, particularly in the GFGP’s major counties. Significant improvements were made to ecosystem services such as carbon sequestration, soil conservation, and habitat quality. The GFGP enhanced the synergistic linkages between ecological services. The implementation of the GFGP decreased water yield, suggesting trade-offs with other ecosystem services. Additionally, we investigate regional trade-offs/synergies between ecosystem services and their influencing factors, which were influenced by topographic and climatic variables. To maximize the benefits of ecological restoration efforts, we need a deeper understanding of the relationships between ecosystem services and the mechanisms that drive them. Thus, policymakers can scientifically exert control over local influences on ecosystem services, either by boosting the provision of specific services or by limiting specific influences in order to maintain ecosystem stability.

Food systems are responsible for a third of global anthropogenic greenhouse gas emissions and there has been an increasing research focus on food-system greenhouse gases. However, limited attention has been paid to emissions from the regional trade network associated with food systems. Here we developed a multi-regional input-output-based hybrid life cycle assessment model and traced China’s food-system greenhouse gas emissions from farm to fork. China’s food system emitted 2.4 (95%; confidence interval range: 1.6–3.2) gigatons CO 2 -equivalent in 2019, and half were emitted at the production stage. There were substantial differences in the emission structure and sources among the provinces. Further analysis indicated that the differences among provinces were caused by the separation of food production and consumption. People living in wealthier coastal and central regions consumed food from western and northeastern regions. Therefore, the government should consider interregional synergies when developing strategies to reduce food-system greenhouse gas emissions.

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.

Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive (PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals (SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index (MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis (ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following: (1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development (R&D) expenditure, and funding per capita for cultural undertakings. (2) From 2007 to 2017, provincial income poverty (IP), health poverty (HP), cultural poverty (CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces. (3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas. (4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.