Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China.

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km2 in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20 °C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 °C is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

Examining the choice of land conflict resolution mechanisms: The case between the harshin and yocaale woredas of the Somali region of Ethiopia.

This study examines the choice of conflict resolution mechanisms between Yocaale and Harshin pastoral communities in the Somali regional state of Ethiopia. Between 1984 and 2014, the area experienced several land use changes, leading to a decrease in the vegetation cover of grass, shrubs, and woodlands. Furthermore, from 2000 to 2005, ten new urban areas have been established. This changed the area's rangeland caring capacity, which, in turn, triggered competition over dwindling resources. Evidence also indicates that climate change has exacerbated the competition for scarce resources in this area. In response, the pastoralist communities began practicing crop cultivation, petty trading, land enclosure, and charcoal production, particularly in the Harshin woreda (district). The 2011 regional administrative restructuring decision created new administrative units and sandwiched the Yocaale pastoralist community between the Harshin and Aware woredas. The decision to establish a new woreda, coupled with the area enclosure and land privatization, impeded the opportunistic movement of the Harshin and Yocaale's pastoralists and became a source of competition for communal land resources. This study used a mixed methods approach of qualitative and quantitative analyses and deployed a binary logit model to identify the two woredas' conflict resolution preferences. The findings indicate that the land disputes between the two woredas, which are mainly on administrative borders conflict, is less likely to be taken to customary institutions to resolve. Instead, they opt to formal court system. In conclusion, even though the findings of this work highlight the customary institution's vital role in preventing and resolving inter-ethnic conflicts, administrative border-related land conflicts are less likely to be taken to customary institutions, as they require political decision-making in multiple contested clan areas, such as Yocaale and Harshin. Therefore, in addition to particular corrective political measures, the results of this study point to the need for enhancing the level of integration and cooperation between the two communities and finding a middle way to enhance community dialogue.

Spatial-temporal evolution of coupling relationship between land development intensity and resources environment carrying capacity in China.

Land development poses challenges to the sustainable use of resources and environmental health in regions. This study explores the coupling relationship and its spatial-temporal evolution trend between land development intensity and resources environment carrying capacity of 31 provinces in China from 2005 to 2017. The information entropy method, coupling degree model, and coupling coordination degree model are used to calculate the index weight, coupling degree, and coupling coordination degree. The results show that: (1) Three change types of resources environment carrying capacity are presented with land development intensity increasing: first decrease and then increase; first increase and then decrease; and alternating fluctuations. (2) The proportion of construction land, GDP per land, and population density are dominate determinants of land development intensity, while the water resources per capita, energy consumption per unit of GDP, and per capita cultivated land area are that of resources environment carrying capacity. (3) From the perspective of temporal evolution, both coupling and coordination relationship were found to have continuously strengthened. (4) In terms of spatial evolution, the coupling level presented a constantly narrowing inter-regional gap, and the coordination level has changed from initial two-level differentiation to final regional gap narrowing. These findings can provide evidence in support for integrating land development with resources environmental protection to promote regional coordinated development.

The spatial spillover effect and nonlinear relationship analysis between land resource misallocation and environmental pollution: Evidence from China.

Compared with other countries, China's local governments often adopt the land supply strategy of "low price and sufficient supply" for industrial land and "high price and limited supply" for commercial land in the allocation of land resources. The allocation of land resources is an important means to promote the rapid development of China's economy, and the impacts of land resource misallocation (LRM) on environmental pollution are increasingly apparent. This paper uses panel data from 30 provinces in China from 2009 to 2018 to discuss the relationship between LRM and environmental pollution. The ratio of the average price of commercial land to the average price of industrial land is used to measure the degree of LRM. The Ordinary Least Squares (OLS), spatial Durbin model (SDM), threshold model, and mediation effect model are used to study the direct effect, spatial spillover effect, nonlinear relationship, and conduction mechanism of LRM on environmental pollution. The results show that LRM significantly aggravated environmental pollution. This conclusion still holds after robustness tests including the substitution of dependent variables and IV estimates. The LRM aggravates environmental pollution through industrial structure and technological progress. Interestingly, the impact of LRM on environmental pollution also has a significant positive spatial spillover effect in adjacent regions. In addition, there is also evidence that the adverse effect of LRM on environmental pollution is nonlinear at different levels of industrial structure and technological progress. The threshold model shows that with the optimization of the industrial structure, the impact of LRM on environmental pollution shows a weakening trend of "inverted V-shaped", and with the advancement of technology, the impact of LRM on environmental pollution presents an "S-shaped" changing trend of "strong-weak-strong".

An interval chance-constrained fuzzy modeling approach for supporting land-use planning and eco-environment planning at a watershed level.

An interval chance-constrained fuzzy land-use allocation (ICCF-LUA) model is proposed in this study to support solving land resource management problem associated with various environmental and ecological constraints at a watershed level. The ICCF-LUA model is based on the ICCF (interval chance-constrained fuzzy) model which is coupled with interval mathematical model, chance-constrained programming model and fuzzy linear programming model and can be used to deal with uncertainties expressed as intervals, probabilities and fuzzy sets. Therefore, the ICCF-LUA model can reflect the tradeoff between decision makers and land stakeholders, the tradeoff between the economical benefits and eco-environmental demands. The ICCF-LUA model has been applied to the land-use allocation of Wujiang watershed, Guizhou Province, China. The results indicate that under highly land suitable conditions, optimized area of cultivated land, forest land, grass land, construction land, water land, unused land and landfill in Wujiang watershed will be [5015, 5648] hm2, [7841, 7965] hm2, [1980, 2056] hm2, [914, 1423] hm2, [70, 90] hm2, [50, 70] hm2 and [3.2, 4.3] hm2, the corresponding system economic benefit will be between 6831 and 7219 billion yuan. Consequently, the ICCF-LUA model can effectively support optimized land-use allocation problem in various complicated conditions which include uncertainties, risks, economic objective and eco-environmental constraints.

How do varying socio-economic factors affect the scale of land transfer? Evidence from 287 cities in China.

With the rapid development of China's social economy, the scale of land transfer has also increased, which has led to a new pattern of urban land space. This article uses global regression of ordinary least squares (OLS), spatial lag model (SLM), spatial error regression model (SEM) and local regression of geographically weighted regression model (GWR), and multi-scale geographically weighted regression model (MGWR) to explore the influence of socio-economic factors on the scale of land transfer. The relationship between these factors and the scale of land transfer varies greatly from region to region. The local model (MGWR) can express the non-stationary relationship between variables, and the regression estimation results are more robust. The results show that total investment in fixed assets (TIFA) and the non-agricultural population (NAP) had significant effects on the scale of land transfer in 2005, with regression coefficients of 0.964 and -0.247, respectively. In 2010, per capita GDP (PCG), population density (PD), proportion of tertiary industry in GDP (PTIG), and TIFA had significant impacts on the scale of land transfer, and the corresponding impact coefficients were 0.413, -0.085, -0.081, and 0.322. In 2015, the variable of PCG had significant impact on land transfer, with the coefficient of 0.048. The influencing factors of the scale of land transfer are changing at different points in time, and the formulation of land transfer policies should be treated differently according to the different socio-economic conditions in each period.

Spatio-Temporal Evolution and Driving Factors of Rural Settlements in Low Hilly Region-A Case Study of 17 Cities in Hubei Province, China.

With the rapid development of the social economy, factors of social and economic development in China's rural areas have been continuously reorganized, and the pattern and distribution of rural residential areas have undergone significant changes. In rural areas, there have been many peculiar phenomena of "reducing people but not reducing land in rural areas, which has caused tremendous pressure on land resource protection. We used geographic detectors and a geographically temporally weighted regression model (GTWR) to explore the rural settlements' evolution and driving mechanism in Hubei Province from 1990 to 2015. The results show that the kernel density of rural settlements decreased from 1.62 villages/km2 in 1990 to 1.60 villages/km2 in 2015. The scale of rural residential patches has obvious regional differentiation characteristics. From southeast to northwest, there is a wave-like distribution structure of "high-low-high-low-high", and the clustering characteristics of "cold and hot spots" are strengthened with time. Based on GTWR analysis, the total rural population, total power of agricultural machinery, and rural electricity consumption have promoted the expansion of rural settlements, with the regression coefficients 0.096, 0.484, and 0.878, respectively. Cultivated land, agricultural output value, and rural labor force have negative impacts on the expansion, the regression coefficients of the village were -0.584, -0.510, and -0.109, respectively.

How do varying socio-economic driving forces affect China's carbon emissions? New evidence from a multiscale geographically weighted regression model.

The increase in carbon emissions has had great negative impacts on the healthy developments of the human environment and economic society. However, it is unclear how specific socio-economic factors are driving carbon emissions. Based on the multiscale geographically weighted regression (MGWR) model, this paper analyzes the impact mechanism of China's carbon emission data during 2010-2017. The results show that (1) during the study period, China's carbon emissions have obvious positive correlations in the spatial distribution, and the spatial autocorrelation of carbon emissions on the time scale has a further strengthening trend. (2) Compared with the results of the geographically weighted regression (GWR) model, the MGWR model is more robust, and the results are more realistic and reliable. The impacts of energy intensity, proportion of green coverage in built-up areas, and industrial structure on provincial carbon emissions are close to the global scale, and their spatial heterogeneity is weak. Other factors have spatially heterogeneous impacts on carbon emissions with different scale effects. (3) Except for proportion of green coverage in built-up areas, the industrial structure and trade openness have insignificant impacts on carbon emissions, but other variables have significant impacts. The total population, urbanization rate, energy intensity, and energy structure have positive impacts on carbon emissions, while the GDP per capita and foreign direct investment have negative impacts on it. This study shows that the main socio-economic factors have different degrees of impacts on carbon emissions with different scale, and we can refer to it to formulate more scientific measures to reduce carbon emissions.

Land Suitability Evaluation and an Interval Stochastic Fuzzy Programming-Based Optimization Model for Land-Use Planning and Environmental Policy Analysis.

People explosion and fast economic growth are bringing a more serious land resource shortage crisis. Rational land-use allocation can effectively reduce this burden. Existing land-use allocation models may deal with a lot of challenges of land-use planning. This study proposed a hybrid quantitative and spatial optimization land-use allocation model that could enrich the land-use allocation method system. This model has three advantages compared to former methods: (1) this model can simultaneously solve the quantitative land area optimization problem and spatial allocation problem, which are the two core aspects of land-use allocation; (2) the land suitability assessment method considers various geographical, economic and environmental factors which are essential to land-use allocation; (3) this model used an interval stochastic fuzzy programming land-use allocation model to solve the quantitative land area optimization problem. This model not only considers three uncertainties in the natural system but also involves various economic, social, ecological and environmental constraints-most of which are specifically put into the optimization process. The proposed model has been applied to a real case study in Liannan county, Guangdong province, China. The results could help land managers and decision makers to conduct sound land-use planning/policy and could help scientists understand the inner contradiction among economic development, environmental protection, and land use.

Influences of different land use spatial control schemes on farmland conversion and urban development.

Land use planning is always officially implemented as an effective tool to control urban development and protect farmland. However, its impact on land use change remains untested in China. Using a case study of Hang-Jia-Hu region, the main objective of this paper was to investigate the influence of different land use spatial control schemes on farmland conversion and urban development. Comparisons of farmland conversion and urban development patterns between the urban planning area and the non-urban planning area were characterized by using remote sensing, geographical information systems, and landscape metrics. Results indicated that farmland conversion in the non-urban planning area was more intensive than that in the urban planning area, and that farmland patterns was more fragmented in the non-urban planning area. Built-up land patterns in the non-urban planning area showed a trend of aggregation, while those in the urban planning area had a dual trend of fragmentation and aggregation. Existing built-up areas had less influence on built-up land sprawl in the non-urban planning area than that in the urban planning area. Built-up land sprawl in the form of continuous development in the urban planning area led to farmland conversion; and in the non-urban planning area, built-up land sprawl in the form of leapfrogging development resulted in farmland areal declines and fragmentation. We argued that it is a basic requirement to integrate land use plans in urban and non-urban planning areas for land use planning and management.

Locational determinants of emissions from pollution-intensive firms in urban areas.

Industrial pollution has remained as one of the most daunting challenges for many regions around the world. Characterizing the determinants of industrial pollution should provide important management implications. Unfortunately, due to the absence of high-quality data, rather few studies have systematically examined the locational determinants using a geographical approach. This paper aimed to fill the gap by accessing the pollution source census dataset, which recorded the quantity of discharged wastes (waste water and solid waste) from 717 pollution-intensive firms within Huzhou City, China. Spatial exploratory analysis was applied to analyze the spatial dependency and local clusters of waste emissions. Results demonstrated that waste emissions presented significantly positive autocorrelation in space. The high-high hotspots generally concentrated towards the city boundary, while the low-low clusters approached the Taihu Lake. Their locational determinants were identified by spatial regression. In particular, firms near the city boundary and county road were prone to discharge more wastes. Lower waste emissions were more likely to be observed from firms with high proximity to freight transfer stations or the Taihu Lake. Dense populous districts saw more likelihood of solid waste emissions. Firms in the neighborhood of rivers exhibited higher waste water emissions. Besides, the control variables (firm size, ownership, operation time and industrial type) also exerted significant influence. The present methodology can be applicable to other areas, and further inform the industrial pollution control practices. Our study advanced the knowledge of determinants of emissions from pollution-intensive firms in urban areas.