• Energy Research
• 1. No poverty close

Energy demand modelling is essential for reliable informing and framing energy policy decisions. More accurate modelling betters ensuring availability of energy and energy quality. Energy availability is related to energy access across the country and defines important economic measures such as energy poverty, which plays a critical role in developing countries. Energy quality is related to the reliability of the supply for correctly estimating energy needs. To incorporate spatial and temporal components of energy in a way that availability and quality are accurately assessed, this article discussed a number of suitable task methods for this (Second-generation GAMs with one-dimensional smoothers: Cyclic/Non-Cyclic Cubic Splines and two-dimensional smoothers: Markov Random Fields/Tensor Splines Interactions). The results showed that the complete consideration of both temporal and spatial aspects leads to a better fitted model which explains more of the data variation.

We aim to investigate the impact of per capita income and high technology product export on CO2 emissions in G20 countries (except Russia) from 1992 to 2014 via panel data analysis. We applied random coefficient panel regression method to capture the heterogeneity of the G20 countries. The findings indicate that per capita income has stronger impact on CO2 emission level in developing countries whereas high technology export has stronger impact on CO2 emission level in developed countries. We interpret this result in the way that developing countries increase their CO2 emissions through increased income and consumption whereas developed countries do so via production and export.

Energy poverty affects billions worldwide, including people in developed and developing countries. Identifying those living in energy poverty and implementing successful solutions require timely and detailed survey data, which can be costly, time-consuming, and difficult to obtain, particularly in rural areas. Through machine learning, this study investigates the possibility of identifying vulnerable households by combining satellite remote sensing with socioeconomic survey data in the UK. In doing so, this research develops a machine learning-based approach to predicting energy poverty in the UK using the low income low energy efficiency (LILEE) indicator derived from a combination of remote sensing and socioeconomic data. Data on energy consumption, building characteristics, household income, and other relevant variables at the local authority level are fused with geospatial satellite imagery. The findings indicate that a machine learning algorithm incorporating geographical and environmental information can predict approximately 83% of districts with significant energy poverty. This study contributes to the expanding body of research on energy poverty prediction and can help shape policy and decision-making for energy efficiency and social fairness in the UK and worldwide.

In this study, we investigate how far away and for how long past flooding affected single-family housing values in Gyeonggi, South Korea. In order to empirically explore the geographic and temporal extent of the effects, we adopt two analytical methods: random-intercept multilevel modeling and Mahalanobis-metric matching modeling. The analytical results suggest that the geographic extent of the discount effect of a flooding disaster is within 300 m from an inundated area. Market values of housing located 0–100, 100–200, and 200–300 m from inundated areas were lower by 11.0%, 7.4%, and 6.3%, respectively, than counterparts in the control group. The effect lasted only for 12 months after the disaster and then disappeared. During the first month, 1–3 months, and 3–6 months after a flood, housing units in the disaster-influenced area (within 300 m of the inundated area) were worth, on average, 57.6%, 49.2%, and 45.9% less than control units, respectively. Also, within the following 6 months, the discount effects were reduced to 33.2%. On the other hand, the results showed no statistically significant effects on market values more than 12 months after the disaster. By providing insights into how people perceive and respond to natural hazards, this research provides practical lessons for establishing sustainable disaster management and urban resilience strategies.

Under the strong support of policies and incentives, the global electric vehicle (EV) market has been developing rapidly. However, in the context of the overall EV market boom, the promotion policies and incentives for consumers to adopt EVs differ from country to country. It is worth exploring the key factors that affect market share and adoption of EVs, such as incentives, policies, and additional socio-economic factors. The data on EV market share and information on policies and incentives in 20 countries were collected from the published reports and online resources from 2015 to 2019. Random effects model analysis was conducted to explore the effect of various factors on EV market share. The innovation of this article is to combine incentive policies with socio-economic factors and use panel data to analyze the actual adoption behavior of the global EV market. Results show that the tax reduction policy, charger density, and income have significantly positive effects on the penetration of EVs. Thus, it suggested that government should still maintain tax incentives and focus on the deployment of charging infrastructure. Household income, as a socio-economic factor, also plays an important role in the adoption of EVs. This will help policymakers adjust and improve policy emphasis to promote the adoption of EVs.

The rapid development of digital technologies and massive data analytics has enabled artificial intelligence (AI), via “machine learning”, to impact many societal sectors, including the wholesale and retail trade (WRT). However, the specific impact pathway and dynamics are still unclear. Based on the panel data of 30 provinces in China from 2015 to 2021, this paper employed the “VHSD-EM” model, random forest algorithm, and partial effect analysis to build an evaluation index system of AI and WRT, then to study the impact of AI on WRT in the temporal and spatial dimensions. Our main discoveries were as follows: (1) the quality of the WRT aligned well with the relative level of AI in the provinces, although the latter developed at a relatively fast pace; (2) the shortcomings that hindered the quality of WRT development varied in different regions, with a stark mismatch between the degree of informatization and the level of economic development in the eastern coastal region, a lack of innovation in the relatively high economic presence of the northern provinces, and a weak sharing of resources in the western region; (3) AI enhanced WRT development jointly with other key factors, particularly the density of employment, the percentage of WRT employees, and the ratio of the year-end financial institution deposits to the regional GDP, which raises the importance of the transaction volume of the technology market; (4) spatial differences exist in the impact pathways of AI on the high-quality development of WRT, and, for most provinces and regions except Shanghai and Guangdong, there is still significant room for expansion in the utilization of AI in WRT.

China’s economic growth has been impressive, but the persistent income inequality poses a threat to its sustainability. To address this issue, we use the complex network analysis method for the first time to explore the structural characteristics of the regional spatial correlation network of inclusive growth (RSCN) of 26 provinces (autonomous regions and municipalities) in China from 1999 to 2020. We use exponential random graph models to explore the internal mechanisms and driving factors that shape this network. Our results show that inclusive growth dependencies between regions are widespread and increasing. Beijing, Shanghai, Jiangsu, and Zhejiang serve as benchmark regions, while provinces in the middle reach of the Yangtze River play an increasingly important bridging role. The northwestern region mainly acts as a receiving region. Our study identifies transitivity, reciprocity, and high interaction tendency as critical microstructures. Furthermore, we find that infrastructure, digital economy development, financial marketization, fiscal expenditure linkages, and inter-provincial trade linkages are crucial factors in shaping network relationships. Our study provides theoretical support for the development of China’s regional coordinated development strategy and sustainable economic growth policies.

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.