The past decades were witnessing unprecedented habitat degradation across the globe. It thus is of great significance to investigate the impacts of land use change on habitat quality in the context of rapid urbanization, particularly in developing countries. However, rare studies were conducted to predict the spatiotemporal distribution of habitat quality under multiple future land use scenarios. In this paper, we established a framework by coupling the future land use simulation (FLUS) model with the Intergrated Valuation of Environmental Services and Tradeoffs (InVEST) model. We then analyzed the habitat quality change in Dongying City in 2030 under four scenarios: business as usual (BAU), fast cultivated land expansion scenario (FCLE), ecological security scenario (ES) and sustainable development scenario (SD). We found that the land use change in Dongying City, driven by urbanization and agricultural reclamation, was mainly characterized by the transfer of cultivated land, construction land and unused land; the area of unused land was significantly reduced. While the habitat quality in Dongying City showed a degradative trend from 2009 to 2017, it will be improved from 2017 to 2030 under four scenarios. The high-quality habitat will be mainly distributed in the Yellow River Estuary and coastal areas, and the areas with low-quality habitat will be concentrated in the central and southern regions. Multi-scenario analysis shows that the SD will have the highest habitat quality, while the BAU scenario will have the lowest. It is interesting that the ES scenario fails to have the highest capacity to protect habitat quality, which may be related to the excessive saline alkali land. Appropriate reclamation of the unused land is conducive to cultivated land protection and food security, but also improving the habitat quality and giving play to the versatility and multidimensional value of the agricultural landscape. This shows that the SD of comprehensive coordination of urban development, agricultural development and ecological protection is an effective way to maintain the habitat quality and biodiversity.

Air pollution contributes to a large fraction of the total mortality estimated under the global burden of disease project (GBD) of the World Health Organization (WHO). This paper discusses an integrated study to obtain the spatiotemporal characteristics of particulate matter (PM₁₀ and PM_2.5) and trace gases (O₃, SO₂, NO₂, and CO) pollutants in Hangzhou City (China) for the years 2014–2016. Our detailed analysis shows a relationship between air pollutants and land-use/land-cover change. Air quality parameters (PM_2.5 and PM₁₀) and trace gases (SO₂, NO₂, and CO) show strong monthly variations in the months of January (higher values) and July (lower values). During monsoon and summer seasons, air quality and trace gases show low values, whereas ozone (O₃) is higher in the summer and lower in the winter. The spatial distribution of air pollutants is retrieved using the kriging method at the monitoring sites in Hangzhou City. We have considered normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Landsat 8 data. The correlation between air pollutants and land use at the street-town unit suggests that areas with low NDVI, high road density, large built-up density, and LST are consistent with high concentrations of particulate matter and SO₂, NO₂, and CO. Among the trace gases, NO₂ is found to be the most sensitive element affected by land use patterns, and O₃ shows weak correlation with land use. SO₂ shows a strong positive correlation with road density and LST, whereas CO shows positive correlation with the built-up density, LST, and population density.

Climate change has gained widespread attention, and the rapid growth of the economy in China has generated a considerable amount of carbon emissions. Zhejiang Province was selected as a study area. First, the energy-related carbon emissions from 2000 to 2014 were accounted for, and then the Logarithmic Mean Divisia Index (LMDI) decomposition model was applied to analyse the driving factors underlying the carbon emissions. Finally, three scenarios (inertia, comparative decoupling and absolute decoupling) for 2020 and 2030 were simulated based on the low-carbon city and Human Impact Population Affluence Technology (IPAT) models. The results showed (1) carbon emissions increased by 1.66 times from 2000 to 2014, and trends of carbon emissions were used to divide the study period into three phases (rapid, medium growth and slow decrease phases, with annual growth rates of 12.60%, 4.77% and −1.24%, respectively); (2) the energy intensity effect from 2000–2011 inhibited carbon emissions but was exceeded by the economic output effect, which increased emissions, whereas the energy intensity effect from 2011–2014 outweighed the economic output effect; (3) the scenario analyses revealed that both the comparative and absolute decoupling scenarios would remain consistent with the carbon emissions boundaries in 2020 and 2030, but the comparative decoupling scenario was more reasonable for sustainable development. In addition, appropriate design of emission trading scheme could help to achieve the comparative decoupling by financial incentives.

Water can carry or overturn a boat. Natural resources form the foundation of human survival and development. However, land use change caused by human urban civilization has damaged the natural environment and in turn threatened the continuation of human civilization. Accordingly, it is crucial to analyze the impacts of human activities on land use change and consequent dynamics of ecosystem service value (ESV). For the sustainable development of human beings, an investigation should be conducted to explore what type of land use behavior will be considerably beneficial to improve our relationship with the natural environment. This study analyzes the spatial–temporal dynamics of ESV of 148 counties in the Yangtze River Delta in China over three five-year periods (2000–2015) and examines the influence of socioeconomic forces and policy implications. Exploratory spatial data analysis and spatial regression were applied to facilitate the analysis. Results show that the averages of the ESV change ratios of the 148 counties in each of the aforementioned periods are −0.667%, −2.690%, and −4.668%, respectively. The number of counties that showed an ESV loss trend in the three periods are 72 (48.6%), 125 (84.4%), and 139 (93.9%), respectively. In terms of spatial pattern, ESV change demonstrates the spatial distribution characteristic of “high loss spreading from the northeast to the middle and west” with a tendency to strengthen spatial agglomeration. Results of the spatial regression analysis determine the overwhelming importance of population growth and economic advancement. The results also indicate that the development mode characterized by industrial structure, capital input, and technology upgrades can exert considerable influence on socioeconomic development, thereby affecting the change of ESV. Moreover, the constraints of policy substantially affect the changes of ESV from 2010 to 2015. Policy makers should consider the relationship between land use patterns and the ESV variation in different development stages to formulate appropriate measures, thereby reducing or preventing the loss of ecological service value and promoting sustainable development.