The last couple of decades have witnessed growing interest in the academic literature in the conciliation of finance and sustainable development. Foreign direct investment (FDI) faces increasing pressure from both host and home country towards adoptinsg sustainable approaches. Such solutions can be green innovation (GI) for climate change, environmental risks, green processes and products that allow tracking the carbon footprint, as well as many other green technologies. Based on the macro-level data of 31 provinces in China from 2003 to 2020, this paper employed policy environment (PE) and marketization level (ML) as moderating variables to further investigate the impact of FDI on GI. Our results show the following: (1) FDI has a significant positive and dynamic evolution feature of diminishing marginal efficiency on GI. (2) The heterogeneity analysis of regional regression shows that FDI significantly increases GI in the eastern and western regions. In contrast, FDI in the central region inhibits GI but not significantly. (3) Both PE and ML can positively moderate the impact of FDI on GI. Furthermore, our empirical results of the robustness test of 2SLS and GMM are highly consistent with the main test. The conclusions of this paper provide policy implications for local governments to fully and effectively utilize foreign capital for green innovation activities.

The cloud-free monthly composite of the global nighttime light (NTL) data derived from the Suomi National Polar orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has gained popularity for detecting anthropogenic and socioeconomic activities. However, the monthly VIIRS DNB composite suffers from a data missing problem induced by continuous cloud cover. The full potential of the VIIRS DNB time series is consequently hindered by low-quality and missing observations. This article proposes a spatiotemporal statistical method (STSM) to predict the VIIRS DNB imagery in severe absence of valid observations' situation. The polynomial with the harmonic model was applied to describe the long-term trends and seasonal cycles in time series. A spatial marginal semivariogram was established to quantify the data dependence in space; we then used spatial interpolation to correct the predicted results from temporal curve fitting. The final predicted values were validated with the actual values based on cross-validation. The results suggest that the STSM is suitable for predicting with a high coefficient of determination (R² = 0.922) and a relatively low root-mean-square error (RMSE = 3.40 nW/cm²/sr). We extended the proposed method to forecast future imagery for a five-month period, the performance of which was more stable, with the highest R²/RMSE (0.158 ± 0.010), compared with two other methods. Therefore, the STSM is effective and stable for modeling and predicting the VIIRS DNB monthly composite and will help address the data missing issue.

Understanding the spatiotemporal patterns and key determinants of rural homestay industry agglomeration is crucial for the well-planning and well-management of rural tourism during the process of rural revitalization in China. By employing multi geospatial datasets, this study investigated the long-term spatiotemporal patterns and their key determinants of homestay inns during the period 2004–2019 in Moganshan, a well-known rural tourism destination in Zhejiang Province, China. The kernel density estimation and spatial autocorrelation were integrated to identify the hotspots of rural homestay inns at a fine scale. The key determinants were further uncovered using multiple stepwise regression and logistic regression models. The result shows that the overall growth of homestay inns was slow at the early stage and has progressed rapidly since 2014, with 94.2% of homestay inns newly opened during the period 2014–2019. The first hotspot was located in Moganshan National Park and then spread to the surrounding villages. Three hotspot zones have emerged, including the northern hotspot zone (Sihe-Xiantan), central hotspot zone (Houwu-Park-Liaoyuan), and southern hotspot zone (Ziling-Laoling-Lanshukeng) by 2019. The modeling indicates that government policy was an essential determinant for the increase in homestay inns, followed by entrepreneurship and investment. The new homestay inns were more likely to occur in settlements close to scenic spots, river networks, and cultivated land. Abundant scenic spots and heterogeneous landscapes were also preferred when selecting sites and executing landscape design for homestay inns. Our empirical study has provided practical insights for policy makers, entrepreneurs, and planners for future sustainable homestay industry development.

China has experienced rapid industrial growth over the last three decades, leading to diverse social and environmental issues. In the new industrialization era, it is urgent to quantify industrial land use (ILU) dynamics for sustainable industrial management, yet there have been limited attempts to systematically quantify these changes, especially in large-scale areas. Through points-of-interest (POIs), a free access geospatial big data, we developed a new framework for exploring ILU dynamics in the mega Hangzhou Bay region (MHBR). The ILU was identified by using natural language processing to mine the semantic information of industrial POIs from 2005, 2011, and 2016. Then, a two-step approach that integrated statistical analysis and hotspots detection was introduced to quantify the changes. The results revealed that traditional industries such as textile products and apparel manufacturing, unspecific equipment manufacturing, and electrical machinery and components manufacturing were dominant types across MHBR, with the enterprise number reaching 14,543, 9412, and 4374, respectively, in 2016. The growth rates of these traditional industries dropped during 2011–2016, while the growth rates of new industries such as Internet information industry and logistics industry increased remarkably, particularly in Hangzhou and Ningbo. Additionally, traditional industrial factories mainly expanded in the urban periphery and coastal zones, whereas new industries mainly grew in the urban center. Shrinkages in the hotspots of traditional industries between 2011 and 2016 were also observed. Our study provides a detailed spatial view of ILU, indicating that MHBR has undergone an industrial transition from traditional industry to new industry.

China has experienced rapid industrial land growth over last three decades, which has brought about diverse social and environmental issues. Hence, it is extremely significant to monitor industrial land and intra-structure dynamics for industrial land management and industry transformation, but it is still a challenging task to effectively distinguish the internal structure of industrial land at a fine scale. In this study, we proposed a new framework for sensing the industrial land and intra-structure across the urban agglomeration around Hangzhou Bay (UAHB) during 2010–2015 through data on points of interest (POIs) and Google Earth (GE) images. The industrial intra-structure was identified via an analysis of industrial POI text information by employing natural language processing and four different machine learning algorithms, and the industrial parcels were photo-interpreted based on Google Earth. Moreover, the spatial pattern of the industrial land and intra-structure was characterized using kernel density estimation. The classification results showed that among the four models, the support vector machine (SVM) achieved the best predictive ability with an overall accuracy of 84.5%. It was found that the UAHB contains a huge amount of industrial land: the total area of industrial land rose from 112,766.9 ha in 2010 to 132,124.2 ha in 2015. Scores of industrial clusters have occurred in the urban-rural fringes and the coastal zone. The intra-structure was mostly traditional labor-intensive industry, and each city had formed own industrial characteristics. New industries such as the electronic information industry are highly encouraged to build in the core city of Hangzhou and the subcore city of Ningbo. Furthermore, the industrial renewal projects were also found particularly in the core area of each city in the UAHB. The integration of POIs and GE images enabled us to map industrial land use at high spatial resolution on a large scale. Our findings can provide a detailed industrial spatial layout and enable us to better understand the process of urban industrial dynamics, thus highlighting the implications for sustainable industrial land management and policy making at the urban-agglomeration level.

Population flow caused by rapid urbanization has driven the urban-rural construction land transition. A systematic recognition of their interaction is essential for achieving coordinated and sustainable urban-rural development. We chose Hangzhou, one of the most urbanized cities in China, as the study area to analyze this interaction from the perspectives of dominant morphology and recessive morphology during the period of 2010 to 2020. From the perspective of dominant morphology, a structured analysis method is applied to assess the variation in the internal components. A dynamic land utilization efficiency index is proposed and combined with an original static land utilization efficiency index to comprehensively portray a complete periodicity of urban-rural construction land transition and population flow from the perspective of recessive morphology. Results demonstrate that the path dependence of rural industrialization leads to a seemingly stagnant urban-rural construction land transition in Hangzhou. Meanwhile, the rapid increase in the demographic urbanization rate has been supported by the massive inflow of external migrants. Based on the more stringent dynamic land utilization efficiency index and the more comprehensive combination of static and dynamic indices, the moderate expansion of urban construction land increases the proportion of township-level units of the intensive land utilization pattern while, instead of population loss, the general non-intensive land utilization pattern in rural areas has been driven by the overexpansion of rural residential land.