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With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

Rapid urbanization complicates the precise, timely quantification of urban spatial morphology. This study examined urban spatial morphology in Fujian Province, integrating DMSP-OLS and NPP-VIIRS nighttime light imagery from 1992 to 2022 to extract the built-up urban footprint via the constructed VMNUI. This method achieved an overall accuracy >0.95 and a Kappa coefficient of 0.80 when the results were compared against land use samples. Utilizing Centroid Migration Analysis, clustering, Geographical Detector, and GTWR, we quantitatively analyzed Fujian’s urban spatial form and its driving mechanisms. The results indicate that the calibration and integration of NTL data effectively resolved saturation and overflow issues in the DMSP data, revealing an urban expansion rate of 3.79%, which centered on coastal areas. Geographical Detector analysis identified fixed-asset investment (q = 0.83), population (0.80), precipitation (0.78), and highway density (0.76) as dominant factors; GDP ∩ fixed-asset investment yielded the strongest interaction (0.873). GTWR further identified that slope aspect, GDP, and secondary industry share accelerated expansion in eastern Fujian, whereas population, urbanization rate, and mean temperature were key drivers of expansion in the west. This study analyzed the spatiotemporal evolution patterns and driving mechanisms of urban spatial form development in Fujian Province over a long period, and based on the results, actionable, science-based optimization strategies with practical implications are proposed for sustainable development in the region.

It is crucial for comprehending the developmental trend of the agricultural economy, refining the agricultural industrial structure, and amending agricultural policies to accurately forecast and timely obtain the total output value of agriculture, forestry, animal husbandry, and fishery (TOVAFAF). This paper attempts to utilize eight nighttime light (NTL) indices originally constructed based on NPP-VIIRS NTL remote sensing data serving as multiple input variables to establish a more accurate and effective forecasting model for the TOVAFAF in various provinces of China. For the major challenge of characterizing the complex nonlinear relationship between NTL data and the TOVAFAF under the condition of limited samples, this paper employed single-hidden-layer back propagation (BP) neural network and extreme learning machine (ELM) as two basic modeling methods, proposed a novel ensemble particle swarm optimization (EPSO) algorithm as optimization mechanism for neural network models to overcome the limitation of traditional particle swarm optimization (PSO) algorithm, and used logarithmic transformation to enhance the correlation between NTL data and the TOVAFAF. The experimental results further substantiate that the neural network algorithms can effectively characterize the potential nonlinear relationship between NTL data and the TOVAFAF. And the neural network models optimized by the EPSO mechanism, those under logarithmic transformation, and the BP neural network series models exhibit superior forecasting performance than those optimized by the PSO mechanism, those under normalization, and the ELM series models, respectively. Furthermore, the EPSO-BP model under logarithmic transformation provides the best forecasting performance on the TOVAFAF for China’s various provinces in 2023, with the mean relative error (MRE) of 20.65% and the determination coefficient (R 2 ) of 0.8749 for the linear relationship between the actual and forecasting values, which presents a decrease of 11.55 percentage points in MRE and an increase of 35.38% in R 2 compared to the PSO-ELM model in our previous research.

The development of a low-carbon economy is essential for current economic growth. To gain a comprehensive understanding of the spatiotemporal evolution of low-carbon economy development in China’s Yangtze River Delta and to clarify its influencing factors, this research employs spatial analysis methods such as kernel density estimation, Moran’s I, spatial Markov chains, and the spatiotemporal geographically weighted regression model. The study draws the following conclusions: (1) The overall development of the low-carbon economy in the region is improving, though there are notable spatial and temporal differences between cities. In recent years, the number of cities with high levels of low-carbon development has steadily increased, yet provincial disparities remain, with Anhui Province facing the most prominent development challenges. (2) Low-carbon economy development in the Yangtze River Delta shows clear spatial clustering and migration patterns. The number of cities in a low-low agglomeration state remains stable, while the number of high-high agglomeration cities has slightly decreased. Spatially, cities with high-high agglomeration are concentrated in the eastern part of the region, while low-low agglomeration is mainly found in the west. (3) Various factors influence the development of the low-carbon economy in the Yangtze River Delta. The average regression coefficients for industrial upgrading, government intervention, technological advancement, education, and economic development are all positive, the effect of human capital shows stage-specific characteristics.

Poverty has emerged as one of the chronic dilemmas facing the development of human society during the twenty first century. Accurately identifying regions of poverty could lead to more effective poverty-alleviation programs. This study used a new type of remote-sensing data, NPP-VIIRS, to locate poverty-stricken areas based on nighttime light, taking Chongqing Municipality as a sample, and constructed a multidimensional poverty index (MPI) system, guided by a well-known and widely used conceptual framework of sustainable livelihood. A regression model was constructed and results were correlated with those using the average nighttime light index. The model was then tested on Shaanxi Province, and average relative error of the estimated MPI was only 11.12%. These results showed that multidimensional poverty had a high spatial concentration effect at the regional scale. We then applied the index nationwide, at the county scale, analyzing 2852 counties, which we divided into seven classifications, based on the MPI: extremely low, low, relatively low, medium, relatively high, high, and extremely high. Eight hundred forty-eight counties in 26 provinces were identified as multidimensionally poor. Among these, 254 were absolutely poor counties and 543 were relatively poor counties; 195 of these are not on the list of poverty-stricken counties as identified by income levels alone. By improving the accuracy of targeting, this method of identifying multidimensional poverty areas could help the Chinese government improve the effectiveness of poverty reduction strategies, and it could also be used as a reference for other countries or regions that seek to target poor areas that suffer multidimensional deprivation.

Precision in discerning the spatiotemporal dynamics of municipal solid waste (MSW) production and its drivers is pivotal for informing the seasonal management and recycling of urban waste streams. This investigation zeroed in on Hefei’s central urban zone, deploying a nuanced principal component analysis and geographically and temporally weighted regression (PCA-GTWR) to quantify the sway of the environmental, economic, and living standard variables on the MSW generation patterns. The methodology unfolded across four main phases: (1) leveraging nocturnal light data to approximate the MSW output; (2) employing spatial autocorrelation to probe the variable trends and spatial interdependencies of the waste generation; (3) harnessing principal component analysis to pinpoint critical determinants and preprocess these as inputs for the GTWR model; (4) mapping the GTWR outcomes to elucidate the differential impacts of various factors on the waste production patterns. Key findings reveal a distinctively polycentric MSW distribution, with high-density areas anchored in the urban core and diminishing intensities beyond the secondary periphery. The trio of socioeconomic variables, residents’ living standard variables, and natural variables emerge as pivotal, with the PCA-GTWR offering a vivid spatial delineation of their effects. Notably, socioeconomic growth exerts a pronounced positive influence in more affluent quarters, residential standards bear greater relevance in burgeoning urban sections than in the established core, and environmental influences wield the least sway, ebbing and flowing with the seasons. These insights demystify the undercurrents shaping the MSW production in urban China, serving as a strategic compass for waste minimization initiatives and policy formulation.

Investigating regional connections and their influencing factors from the perspective of “flow” space is one of the foundations of promoting regional development. In this article, the data we used includes actual highway passenger flow data, National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light remote sensing data, and socioeconomic data. We analyzed the spatial distribution pattern, connection intensity and spatial autocorrelation of highway passenger flow in Chongqing during the working day, weekend and May Day and revealed the influencing factors by means of a geographic detector. Three key conclusions resulted from this research. First, highway passenger flow in Chongqing districts exhibits spatial agglomeration that is clearly higher in western Chongqing than in eastern Chongqing and forms an obviously dual-core “star” structure, with the main urban area and Wanzhou serving as the core. Second, a factor detector notes that the nighttime light area index (0.9251, 0.9512, 0.9541) has the strongest explanatory power for the spatial differentiation of passenger traffic in Chongqing districts, which is the key factor. Third, interaction detection shows that the interaction between the two factors displays an enhancement effect at different times. The nighttime light area index shows the strongest explanatory power under the synergy of tourist attractions, which are 0.9850, 0.9903 and 0.9908. But the per capita GDP and highway mileage have the most obvious enhancement effect after interaction (0.9544, 0.9661, 0.9652). Therefore, in future planning and development, we should pay attention to cooperation and exchanges between districts and use the nighttime light area index as an important reference factor to provide a scientific basis for the construction of public transport and economic construction in Chongqing.

This paper explores the use of spatiotemporal geographic information and advanced technology to effectively address the issue of poverty reduction and development. The focus is on Hebei Province, where multi-dimensional poverty identification and spatiotemporal evolution analysis are conducted using nighttime light remote sensing data. The study establishes a Multi-dimensional Poverty Index (MPI) system based on the regional average nighttime light index (ANLI) extracted from data spanning 2010, 2014, and 2018. A coupled regression model confirms the correlation between MPI and ANLI. Visualization and analysis are performed using GIS technology, Moran's I, and Getis's G* to interpret the identification results. From the experimental results, MPI established in this paper fits well with ANLI, which can be used for poverty identification and monitoring. The established multi-dimensional poverty model can identify multi-dimensional poverty counties better. However, there is a large discrepancy in the match with the traditional list of poor counties issued by the state from the perspective of absolute economic poverty. From the perspective of spatiotemporal evolution, it can be seen that the overall poverty level in Hebei Province has changed with time. Although there is aggregation among poverty areas, the aggregation is not deep. The poverty level of the traditional national-level poor counties has also been reduced, but the pattern of poverty aggregation remains unchanged. The \"C-shaped\" poverty belt around Beijing formed by Chengde, Zhangjiakou, Baoding and other surrounding counties in northern Hebei Province is still the focus of poverty alleviation work in the next stage.

Urbanization has been proven to be a critical factor in modifying local or regional climate characteristics. This research aims to examine the impact of urbanization on extreme climate indices in the Yangtze River Economic Belt (YREB), China, by using meteorological observation data from 2000 to 2019. Three main steps are involved. First, a clustered threshold method based on remote-sensing nighttime light data is used to extract urban built-up areas, and urban and rural meteorological stations can be identified based on the boundary of urban built-up areas. Nonparametric statistical tests, namely, the Mann–Kendall test and Sen’s slope, are then applied to measure the trend characteristics of extreme climate indices. Finally, the urbanization contribution rate is employed to quantify the impact of urbanization on extreme climate indices. The results indicate that urbanization has a more serious impact on extreme temperature indices than on extreme precipitation indices in the YREB. For extreme temperature indices, urbanization generally causes more (less) frequent occurrence of warm (cold) events. The impact of urbanization on different extreme temperature indices has heterogeneous characteristics, including the difference in contamination levels and spatial variation of the impacted cities. For extreme precipitation indices, only a few cities impacted by urbanization are detected, but among these cities, urbanization contributes to increasing the trend of all indices.

This paper attempts to establish the accurate and timely forecasting model for the total output value of agriculture, forestry, animal husbandry, and fishery (TOVAFAF) in various provinces of China using NPP-VIIRS nighttime light (NTL) remote sensing data and machine learning algorithms. It can provide important data references for timely assessment of agricultural economic development level and policy adjustment. Firstly, multiple NTL indices for provincial-level administrative regions of China were constructed based on NTL images from 2013 to 2023 and various statistics. The results of correlation analysis and significance test show that the constructed total nighttime light index (TNLI), luminous pixel quantity index (LPQI), luminous pixel ratio index (LPRI), and nighttime light squared deviation sum index (NLSDSI) are highly correlated with the TOVAFAF. Subsequently, using the relevant data from 2013 to 2020 as the training set, the four NTL indices were separately taken as single independent variable to establish the linear model, exponential model, logarithmic model, power exponential model, and polynomial model. And all the four NTL indices were taken as the input features together to establish the multiple linear regression (MLR), extreme learning machine (ELM), and particle swarm optimization-ELM (PSO-ELM) models. The relevant data from 2021 to 2022 were taken as the validation set for the adjustment and optimization of the model weight parameters and the preliminary evaluation of the modeling effect. Finally, the established models were employed to forecast the TOVAFAF in 2023. The experimental results show that the ELM and PSO-ELM models can better explore and characterize the potential nonlinear relationship between NTL data and the TOVAFAF than all the models established based on single NTL index and the MLR model, and the PSO-ELM model achieves the best forecasting effect in 2023 with the MRE value for 32.20% and the R2 values of the linear relationship between the actual values and the forecasting values for 0.6460.