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Human activities are increasingly pressuring the land system and biosphere integrity, highlighting the urgency to manage this pressure—measured as human appropriation of net primary productivity (HANPP)—within a safe limit (planetary boundary) to avoid catastrophic consequences. Prior studies have suggested that solutions lie in demand management, yet the HANPP consumption by country and its future trends remain unclear. Understanding these aspects is vital for identifying hotspot regions and proposing potential solutions. Here, by developing a model for country-level HANPP consumption accounting and projections, we find that nearly half of high-income countries have surpassed their per-capita HANPP limits. Meanwhile, HANPP consumption in low- and middle-income countries is set to rise rapidly, posing future challenges. We emphasize the necessity for global cooperation in restructuring supply chains and sharing agricultural technologies. Our findings offer insights into how to diminish stresses on the land system and biosphere integrity from the demand side.

Agriculture is one of humankind’s most significant sources of biomass; it also places tremendous pressure on ecosystems through its increasing demand for agricultural products. However, few studies have assessed human pressures on ecosystems from agricultural production and consumption based on a whole-supply-chain perspective. Based on the concept of human appropriation of net primary productivity (HANPP), we evaluate trends of agricultural HANPP embodied in consumption from a global perspective and trace the pressure from agriculture production that is exerted on the environment using an environmentally extended multiregional input–output (MRIO) model. The results show that agricultural HANPP embodied in consumption accounted for over two-thirds of total HANPP but brought about less than 7% of global value added. India, Brazil, and China were found to have the highest level of agricultural HANPP embodied in consumption. Agricultural net exporters were found to usually be low- and lower-income countries, while net importers are found to be high-income countries. According to the driving factor analysis, high-income and low-income countries should cooperate by adjusting consumption patterns and sharing agricultural technology to alleviate the pressure from agricultural production. Our study highlights the importance of agrarian expertise sharing and the need to develop sustainable and green agricultural production.

Ecosystem services are the media and channels through which ecological elements, structures, functions, and products benefit human society. Regulating the utilization intensity and protection methods of society on the ecosystem according to the ecosystem service value (ESV) and its influencing mechanism is of great significance for achieving the sustainable development goals. This paper takes the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the research object and describes the spatiotemporal evolution characteristics of ESV in the GBA from 2000 to 2015. Panel quantile regression is also implemented to increase the understanding of the influencing mechanism of ESV. The main results are as follows: (1) From 2000 to 2015, the total ESV declined with a decreasing rate. The areas of decline were mainly distributed in the central part of the GBA and areas along the Pearl River Estuary. (2) Elasticity index, indicating response of ESV to land use change, reached its peak (1.08). The spatial distribution of elasticity index showed that land use changes brought about more intense ESV variations at the junction of cities. (3) In areas with different ESV levels, the influencing factors have different effects. Land use integrity can only promote ecosystem service capabilities in low-ESV areas. The positive effect of temperature on ecosystem service capacity increases with the increase of ESV, which reflects the self-reinforcement of the ecosystem. Moreover, the negative effect of economic density on ecosystem service capacity decreases with the increase of ESV, which reflects the self-protection of the ecosystem. The combination of such self-reinforcement and self-protection will lead to an ESV gap between the high- and low-ESV areas, and induce the “natural Matthew effect.”

There has been a longstanding debate about the impact of international trade on the environment and human well-being, yet there is little known about such environment and human well-being trade-off. Here, we explore the effect of international trade on the carbon intensity of human well-being (CIWB) globally under the current global trade system and a hypothetical no-trade scenario. We found that between 1995 and 2015, CIWB of 41% of countries declined and 59% of countries increased, caused by international trade, and this resulted in a reduction of the global CIWB and a decline in CIWB inequality between countries. International trade decreased CIWB for high- and upper-middle-income countries and increased CIWB for lower- and middle-income countries. In addition, our results also show that decreases in emission intensity are the most important driver of lower CIWB and the percentage contribution of emission intensity to the improvement in CIWB increases with income. The reduction of emission intensity, population growth, and increase in life expectancy all contribute to CIWB reduction, while the consumption level is the primary factor driving CIWB growth. Our results underscore the importance of studying the impact of international trade on the CIWB of countries at different stages of development.

With the continuous enhancement of regional connectivity, the indirect use of land resources through the pathways of trade in goods and services plays an increasingly important role in the overall utilization of land resources. Despite this, relevant research in this field is still in its infancy, and few papers have addressed this issue. This paper uses a multi-regional input—output model to calculate the embodied land in the 30 provinces (autonomous regions and municipalities) and eight regions of China from the perspective of regional trade and further analyzes the spatial pattern of characteristics associated with embodied land flows. The results show that the amount of embodied land occupied by China’s inter-regional trade accounts for 21.39% of the country’s total land, and an average of 38.54% of China’s provincial land demand is met by land exports from other provinces. More than 80% of land consumed by Beijing, Tianjin, and Shanghai is from other provinces. The provinces of Heilongjiang, Inner Mongolia, Xinjiang, and Hebei are the largest net exporters of cultivated land, forest and grazing and aquatic land, fishery land, and built-up land, respectively (the outflows account for 42.26%, 27.53%, 38.66%, and 35.28% of the corresponding land types in the province); and Guangdong, Shandong, and Zhejiang are the main net importers. The flow of agricultural land (cultivated land, forest land, grazing land, and aquatic land) generally shows a shift from west to east and from north to south. The regions in northeast and northwest China have the largest scale of agricultural land outflows, mainly into East and South China. In addition, 8.43% of cultivated land, 7.47% of forest land, 6.41% of grazing land, 6.88% of aquatic land, and 18.35% of built-up land in China are provided for foreign use through international trade. This paper provides feasible ideas and a theoretical basis for solving the contradiction between land use and ecological protection, which could potentially help to achieve sustainable land use in China.

To achieve carbon peak targets, realize carbon neutrality vision, and tackle global climate change, China must improve the carbon emission performance at the city level. Based on the carbon emission performance of 191 prefecture-level cities in China from 1997 to 2017, this paper analyses the evolution characteristics of urban carbon emission performance from three aspects: the overall spatial and temporal evolution, the differences according to both region and city size, and the differences among clusters categorized by carbon emission performance at the city level. This paper also reveals the impact of the social and economic transition on China’s carbon emission performance. The results show that: (1) The overall level of carbon emission performance of Chinese cities is low, and there is a downward trend during the study period. The differences in carbon emission performance among cities are convergent, but there is a wide gap between high and low values. (2) The carbon emission performance of cities in eastern coastal areas is higher than that in non-coastal areas cities. Large urban agglomerations and economically developed regions, such as provincial capitals, are the agglomeration areas of high urban carbon emission performance values. (3) The carbon emission performance level of cities with similar sizes will converge. At the same time, such changes will enhance the differences among carbon emission performances at the city level within the same region. (4) Cities that belong to high urban carbon emission performance clusters are mainly distributed in the eastern region. Such cities are classified into large cities, supercities, and megacities. Compared with low urban carbon emission performance clusters, cities in high urban carbon emission performance clusters show a higher proportion in the medium-high level and high level of carbon emission performance. Moreover, cities in high urban carbon emission performance clusters are more likely to improve theirurban carbon emission performance. (5) The economic agglomeration effect, industrial structure adjustment and carbon intensity reduction have a significant impact on improving urban carbon emission performance. Population agglomeration has an incremental effect, and the anticipated benefits of environmental regulation have yet to be fully realized. The impacts of different clusters and different regions are variable. Finally, this paper advances policy enlightenment according to its research findings.

Urban resilience is an emerging research topic of urban studies, and its essence is described by the ability of cities to resist, recover, and adapt to uncertain disturbances. This paper constructs a “Size-Density-Morphology” urban ecological resilience evaluation system, uses a coupling coordination degree model to measure the degree of coupling coordination between urbanization and ecological resilience in the Pearl River Delta from 2000 to 2015, and conducts an in-depth discussion on its spatiotemporal characteristics. The results show the following. (1) From 2000 to 2015, the urbanization level of cities in the study area generally increased while the level of ecological resilience declined. The coupling coordination degree between the two systems decreased from basic coordination to basic imbalance. (2) In terms of spatial distribution, the coupling coordination degree between urbanization and ecological resilience of cities presented a circular pattern that centered on the cities at the estuary of the Pearl River and increased toward the periphery. (3) Ecological resilience sub-systems played variable roles in the coupling coordination between urbanization and ecological resilience. Specifically, size resilience mainly played a reverse blocking role; the influence of morphology resilience was generally positive and continued to increase over time; the effect of density resilience was positive and continued to decline and further became negative after falling below zero. The main pathways for achieving coordinated and sustainable development of future urbanization and ecological resilience in the Pearl River Delta include: leading the coordinated development of regions with new urbanization, improving ecological resilience by strictly observing the three areas and three lines, adapting to ecological carrying capacity, and rationally arranging urban green spaces.

Efforts to stabilize the global climate change while also continuing human development depend upon “decoupling” economic growth from fossil fuel CO2emissions. However, evaluations of such decoupling have typically relied on production-based emissions, which do not account for emissions embodied in international trade. Yet international trade can greatly change emissions accounting and reshape the decoupling between emissions and economic growth. Here, we evaluate decoupling of economic growth from different accounts of emissions in each of the 159 countries and analyze the drivers of decoupling. We find that between 1995 and 2015, although 29 countries exhibited strong decoupling of territorial emissions (growing economies and decreasing emissions), only 19 countries achieved economic growth while their consumption-based emissions decreased. Most developed countries have achieved decoupling of emissions related to domestic goods and services, but have not achieved decoupling of emissions related to imported goods and services. The U-test confirms that the domestic component of consumption-based emissions exhibits a stronger decoupling trend from gross domestic product (GDP) growth than consumption-based emissions, and emissions from imports continue to rise with GDP per capita without a corresponding decline, providing a statistical validation of the decoupling analysis. Moreover, in the countries where economic growth and consumption-based emissions are most decoupled, a key driver is decreasing emissions intensity due to technological progress─and especially reductions in the intensity of imported goods and services. Our results reveal the importance of assessing decoupling using consumption-based emissions; successful decoupling may require international cooperation and coordinated mitigation efforts of trading partners.

Urban expansion is one of the main factors driving terrestrial carbon storage (TCS) changes. Accurate accounting of TCS and rigorous quantification of its changes caused by historical urban expansion may help us to better predict its changes in the future. This study focuses on the carbon impacts of urbanization in China where the share of the urban population has increased from 18% in 1978 to 59% in 2017 and the growing will continue in the coming decades. Our results show that China’s TCS decreased at an accelerating pace over the past three decades with an average reduction of 0.72TgC/y in 1980–1990 and 8.72TgC/y in 2000–2010, mostly due to conversion from cropland and woodland to urban land. Through simulating urban expansion under four scenarios from 2010 to 2050, we found a potential increasing trend in land conversion from woodland to urban land. This conversion trend would result in carbon storage loss at an average rate of 9.31TgC/y ∼ 12.94TgC/y in 2010–2050. The increasing trend in both land conversion and carbon storage loss is especially visible in the population centers of the Yangtze River Delta and the Pearl River Delta. Considering that the indirect emission effects of urbanization, such as farmland displacement, population migration, and land degradation, may be much larger, the overall emission impact of forthcoming urban expansion in China would increase the uncertainty of the nation’s carbon emissions and potentially undermine China’s targets as committed in the Paris Climate Agreement.

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.

It is well known that urban areas are typically hotter than the surrounding (vegetated) rural areas. However, the contribution of urbanization to the trends of extreme temperature events such as heat waves (HWs) is less understood. Using a homogenized meteorological dataset drawn from nearly 2,000 stations in China, we find that urban and rural areas have different HW trends and the urban‐rural contrast of HW trends varies across climate regimes. In wet climates, the increasing trends of HWs in urban areas are greater than those in rural areas, suggesting a positive contribution of urbanization to HW trends. In arid regions, the urbanization contribution to HW trends is smaller and even negative. The stronger urbanization contribution to HW trends in wet climates is linked to the smaller variability of urban heat island intensity. This study highlights the important role of local hydroclimate in modulating the urbanization contribution to extreme temperatures. Extreme temperature events commonly known as heat waves (HWs) have profound impacts on human health. While it is well known that urban temperatures are usually higher than their rural counterparts (i.e., the urban heat island effect), whether and how the urbanization contribution to HW trends varies across different climate regimes over a large domain remains unclear. In this study, we explore the urban‐rural contrast of HW characteristics over mainland China. Our analysis shows that while both urban and rural HWs are becoming more frequent, longer‐lasting, and stronger in most parts of China, their trends are different. Interestingly, we find that the local hydroclimate modulates the variability of daily UHI intensity, thus affecting the contribution of urbanization to the frequency and magnitude of HWs. The stronger contrasts between urban and rural HW trends in wet climates are related to the larger increases in UHI intensity, but more importantly, the smaller variability of UHI intensity. As a result, the eastern, wet climate part of China, with the densest population and highest urbanization, will face severe heat risks in the future due to the combined effects of urbanization and global climate change. HWs are becoming more frequent, longer‐lasting, and stronger in most parts of ChinaThe contribution of urbanization to HW trends is larger and positive in wet climates but smaller and even negative in arid climatesThe stronger urbanization contribution to HW trends in wet climates is linked to the smaller variability of urban heat island intensity

This study analyzes the spatial patterns and driving forces of housing prices in China using a 2,872-county dataset of housing prices in 2014. Multiple theoretical perspectives on housing demand, supply, and market, are combined to establish a housing price model to explore the impact of land prices on housing prices. The relative impacts of land prices on housing prices at different administrative levels are then analyzed using the geographical detector technique. Finally, the influencing mechanism of land prices on housing prices is discussed. The main conclusions are as follows. (1) Housing prices have a pyramid- ranked distribution in China, where higher housing prices are linked to smaller urban populations. (2) Land prices are the primary driver of housing prices, and their impacts on housing prices vary over different administrative levels. To be specific, the effect of land prices is the strongest in the urban districts of provincial capital cities. (3) The internal influence mechanisms for land prices driving housing prices are: topographic factors, urban construction level, the agglomeration degree of high-quality public service resources, and the tertiary industrial development level. The urban land supply plan (supply policies) is the intrinsic driver that determines land prices in cities; through supply and demand, cost, and market mechanisms, land prices then impact housing prices.

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation (Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club’ agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.

In this study, housing prices data for residential quarters from the period 2001–2012 were used and Global Differentiation Index (GDI) was established to measure the overall differentiation trend in housing prices in Yangzhou City, eastern China. Then the influence of the natural landscape and environment on prices of global housing market and housing submarkets was evaluated by the hedonic price model. The results are shown as follows. (1) There have been increasing gaps among housing prices since 2001. In this period, the differentiation trend has shown an upward fluctuation, which has been coupled with the annual growth rate of housing prices. (2) The spatial distribution of residential quarters of homogenous prices has changed from clustered in 2001 into dispersed in 2012. (3) Natural landscape and environmental externalities clearly influence spatial differentiation of housing prices. (4) In different housing submarkets, the influence of natural landscape and environmental externalities are varied. Natural landscape characteristics have significant impact on housing prices of ordinary commercial houses and indemnificatory houses, while the impact of environmental characteristics have obvious influence on housing prices of cottages and villas.

Based on patent cooperation data, this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows. The results revealed the spatial structure, composition structure, hierarchical structure, group structure, and control structure of Chinese city network, as well as its dynamic factors. The major findings are: 1) the spatial pattern presents a diamond structure, in which Wuhan is the central city; 2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network, it is weaker than the utility model patent; 3) as the senior level cities, Beijing, Shanghai and the cities in the Zhujiang (Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge; 4) whilst a national technology alliance has preliminarily formed, regional alliances have not been adequately established; 5) even though the cooperation level amongst weak connection cities is not high, such cities still play an important role in the network as a result of their location within ‘structural holes’ in the network; and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity, hierarchical proximity and technological proximity.

Land urbanization plays an important supporting and restriction role in the rapid and sustainable development of urbanization in China, and it shows distinctive spatial heterogeneity. Applying urban area as the basic research unit and urban construction land area as the core indicator, this paper establishes the conceptual framework and calculation method for the quantity and rate of land urbanization process. The study evaluates the spatial differentiation pattern of absolute and relative process of land urbanization in 658 cities in China from 2000 to 2010. The spatial distribution of cities with rapid land urbanization process is discussed, and the contribution rate and its spatial heterogeneity of major land use types are examined with the aid of GIS. The main conclusions are as follows: (1) Land urbanization in China shows a clear spatial difference. The greater the city scale, the faster its land urbanization. The cities with rapid land urbanization show a significant pattern of central distribution in coastal regions and a scattered distribution in the inland regions. (2) Over the last 10 years, the average quantity of land urbanization in the 656 cities was 3.82 km2, the quantity of land urbanization is differentiated by administrative grade. The average rate of land urbanization was 6.89%, obviously faster than the speed of population urbanization. The rate of land urbanization reveals a pattern of differentiation between coastal and other cities. (3) In the past 10 years, the two primary land use types associated with land urbanization in China are residential and industrial, with a combined contribution rate of 52.49%. The greater the scale of the city, the more significant the driving effect of industrial land. In small- and medium-scale cities of the western and central regions, the growth of residential land is the primary driver of land urbanization, while in coastal urban agglomerations and cities on important communication axes, the growth of industrial land is the main driver. (4) Overall, urban population agglomeration, industrial growth and investment are the three drivers of land urbanization in China, but cities of different scales have different drivers.

With rapid urban development in China in the last two decades, the three-dimensional (3D) characteristic has been the main feature of urban morphology. However, the vast majority of researches of urban growth have focused on the planar area (two-dimensional (2D)) expansion. Few studies have been conducted from a 3D perspective. In this paper, the 3D urban expansion of the Yangzhou City, Jiangsu Province, China from 2003 to 2012 was evaluated based on Geographical Information System (GIS) tools and high-resolution remote sensing images. Four indices, namely weighted average height of buildings, volume of buildings, 3D expansion intensity and 3D fractal dimension are used to quantify the 3D urban expansion. The weighted average height of buildings and the volume of buildings are used to illustrate the temporal change of the 3D urban morphology, while the other two indices are used to calculate the expansion intensity and the fractal dimension of the 3D urban morphology. The results show that the spatial distribution of the high-rise buildings in Yangzhou has significantly spread and the utilization of the 3D space of Yangzhou has become more efficient and intensive. The methods proposed in this paper laid a foundation for a wide range of study of 3D urban morphology changes.

Global warming has been one of the major concerns behind the world’s high-speed economic growth. How to implement the coordinated development of the carbon footprint and the economy will be the core issue of the world’s economic and social development, as well as the heated debate of the research at home and abroad in recent years. Based on the energy consumption, integrated with the “Top-Down” life cycle approach and geographically weighted regression (GWR) model, this paper analyzed the spatial differences and multi-mechanism of carbon footprint in provincial China in 2010. Firstly, this study calculated the amount of carbon footprint of each province using “Top-Down” life cycle approach and found that there were significant differences of carbon footprint and per capita carbon footprint in provincial China. The provinces with higher carbon footprint, mainly located in northern China, have large economic scales; the provinces with higher per capita carbon footprint are mainly distributed in central cities such as Beijing, Shanghai and energy-rich regions and heavy chemical bases. Secondly, with the aid of GIS and spatial analysis model (GWR model), this paper had unfolded that the expansion of economic scale is the main driver of the rapid growth of carbon footprint. The growth of population and urbanization also acted as promoting factors for the increase of the carbon footprint. Energy structure had no considerable promoting effect for the increase of the carbon footprint. Improving energy efficiency is the most important factor to inhibit the growing carbon footprint. Thirdly, developing low-carbon economies and low-carbon industries, as well as advocating low-carbon city construction and improving carbon efficiency would be the primary approaches to inhibit the rapid growth of carbon footprint. Moderately controlling the economic scale and population size would also be required to alleviate carbon footprint. Meanwhile, environmental protection and construction of low-carbon cities would evoke extensive attention in the process of urbanization.