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Photovoltaic (PV) power generation has become an important clean energy generation source. In the context of transportation development and its very large energy demand, scholars have begun to use PV power generation technology on roads and their surrounding road spaces. Current research on PV power generation in road spaces has mostly focused on its feasibility and technical potential, but there have been few studies on its economic potential. For this reason, this paper used the Zhengding County of Hebei Province, China, to study the evaluation method of the technical and economic potential of PV power generation in road spaces and to analyze the development potential of PV power generation in road spaces. The results show that Zhengding County has a very high amount of road space available for PV power generation, with an effective PV installation area of 20.98 km2 and an annual theoretical power generation capacity of 1.5 billion kWh. If the PV road space project is fully operational in 2021, it could be profitable by 2026, and the net profit (NP) could reach $705 million in 2030. The application of photovoltaic power generation in road spaces is a very promising method of sustainable energy supply.

Solar radiation is an important input for various land-surface energy balance models. Global solar radiation data retrieved from the Japanese Geostationary Meteorological Satellite 5 (GMS-5)/Visible and Infrared Spin Scan Radiometer (VISSR) has been widely used in recent years. However, due to the impact of clouds, aerosols, solar elevation angle and bidirectional reflection, spatial or temporal deficiencies often exist in solar radiation datasets that are derived from satellite remote sensing, which can seriously affect the accuracy of application models of land-surface energy balance. The goal of reconstructing radiation data is to simulate the seasonal variation patterns of solar radiation, using various statistical and numerical analysis methods to interpolate the missing observations and optimize the whole time-series dataset. In the current study, a reconstruction method based on data assimilation is proposed. Using a Kalman filter as the assimilation algorithm, the retrieved radiation values are corrected through the continuous introduction of local in-situ global solar radiation (GSR) provided by the China Meteorological Data Sharing Service System (Daily radiation dataset_Version 3) which were collected from 122 radiation data collection stations over China. A complete and optimal set of time-series data is ultimately obtained. This method is applied and verified in China’s northern agricultural areas (humid regions, semi-humid regions and semi-arid regions in a warm temperate zone). The results show that the mean value and standard deviation of the reconstructed solar radiation data series are significantly improved, with greater consistency with ground-based observations than the series before reconstruction. The method implemented in this study provides a new solution for the time-series reconstruction of surface energy parameters, which can provide more reliable data for scientific research and regional renewable-energy planning.

Bio-energy from energy plants is expected to play an increasing role in the future energy system, with benefits in terms of reducing greenhouse gas emissions and improving energy security. Pistacia chinensis is believed to be one of the most promising non-food input for biodiesel production. This study focused on the marginal land availability for developing Pistacia chinensis-based bioenergy in China. The spatial distribution, quality and total amount of marginal land resources suitable for cultivating Pistacia chinensis were identified with multiple datasets (natural habitat, remote sensing-derived land use, meteorological and soil data) and geoinformatic techniques. The results indicate that the area of marginal land exploitable for Pistacia chinensis plantations in China is 19.90 million hectares, which may produce approximately 56.85 million tons of biodiesel each year. The spatial variation of both marginal land resources and biodiesel potential are also presented. The results can be useful for national and regional bio-energy planning.

Solar energy is the most clean renewable energy source and has good prospects for future sustainable development. Installation of solar photovoltaic (PV) systems on building rooftops has been the most widely applied method for using solar energy resources. In this study, we developed an approach to simulate the monthly and annual solar radiation on rooftops at an hourly time step to estimate the solar PV potential, based on rooftop feature retrieval from remote sensing images. The rooftop features included 2D rooftop outlines and 3D rooftop parameters retrieved from high-resolution remote sensing image data (obtained from Google Maps) and digital surface model (DSM, generated from the Pleiades satellite), respectively. We developed the building features calculation method for five rooftop types: flat rooftops, shed rooftops, hipped rooftops, gable rooftops and mansard rooftops. The parameters of the PV modules derived from the building features were then combined with solar radiation data to evaluate solar photovoltaic potential. The proposed method was applied in the Chao Yang District of Beijing, China. The results were that the number of rooftops available for PV systems was 743, the available rooftop area was 678,805 m2, and the annual PV electricity potential was 63.78 GWh/year in the study area, which has great solar PV potential. The method to perform precise calculation of specific rooftop solar PV potential developed in this study will guide the formulation of energy policy for solar PV in the future.