Regional distribution of photovoltaic deployment in the UK and its determinants: A spatial econometric approach

Abstract

Photovoltaic (PV) panels offer significant potential for contributing to the UK's energy policy goals relating to decarbonisation of the energy system, security of supply and affordability. The substantive drop in the cost of panels since 2007, coupled with the introduction of the Feed-in Tariff (FiT) Scheme in 2010, has resulted in a rapid increase in installation of PV panels in the UK, from 26.5MWp in 2009 to over 5GW by the end of 2014. Yet there has been no comprehensive analysis of the determinants of PV deployment in the UK. This paper addresses this gap by employing spatial econometrics methods to a recently available data set at a fine geographical detail. Following a traditional regression analysis, a general to specific approach has been adopted where spatial variations in the relationships have been examined utilizing the spatial Durbin model using the cross-sectional data relating to the UK NUTS level 3 data. Empirical results indicate that demand for electricity, population density, pollution levels, education level of households and housing types are among the factors that affect PV uptake in a region. Moreover Lagrange Multiplier test results indicate that the spatial Durbin model may be properly applied to describe the PV uptake relationship in the UK as there are significant regional spillover effects.

Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan

“…In that sense, this study has similarities to Schaffer and Brun (2015), Balta-Ozkan et al (2015), and Dharshing (2017), all of whom applied spatial econometric models. While this study will also apply such model-based analysis, it is expected to obtain clearer insights into the neighborhood influences within a radius of several hundred meters by taking advantage of the census-block level, high spatial resolution data for both the diffusion ratio and social attributes.…”

“…Schaffer and Brun (2015) adopted a spatial lag or spatial autoregression (SAR) model for cross-sectional data, while Dharshing (2017) adopted SAR and spatial error models (SEM) for panel data. Applying a more developed model enabled Balta-Ozkan et al (2015) to investigate the spillover effects of social attributes on diffusion in nearby regions. They applied a spatial Durbin model (SDM) to data from 134 diffusion regions in the UK and showed that the diffusion ratio in a region was positively correlated with the ratio of detached houses and negatively correlated with the average number of household members, not only a single region but also in neighboring regions.…”

Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid

“…The report highlights the need for strong policy intervention in order to achieve the scenarios envisaged. In the UK, the spatial variation in PV adoption and its implications for smart grid scenarios has been touched upon in the literature, but has yet to be fully explored [3,[16][17][18]. In particular, analyses have usually focused on the spatial distribution at a particular snapshot in time, whereas this research looks at the evolution of the spatial distribution in time and uses that to gain insight into the various determinants of that evolution.…”

A Novel Geographical Information Systems Framework to Characterize Photovoltaic Deployment in the UK: Initial Evidence

“…For example, it would seem likely that ground-mounted PV would be more prevalent in areas of low population density (such as rural ones), or that non-domestic PV may be installed to a greater extent in areas with larger coverage of non-domestic buildings. Moreover, despite existing literature provides convincing evidence on the positive correlation between solar irradiance and PV deployment in the domestic sector [14] such evidence still has to be given for non-domestic and ground mounted market segments.…”

“…While this analysis was disaggregated spatially (i.e., it considered trends in individual post-code areas) the authors noted the limitations of assessing such areas in isolation (e.g., by not considering how deployment in one area related to that in an adjacent one), and discussed the value of future work to investigate deployment across adjacent areas. More recently, Ozkan et al [14] investigated how trends in the UK domestic market relates to a number of variables (including the population density, proportion of detached houses and irradiation), spatially disaggregating the UK to 134 areas. This research highlighted the importance of both local features of the built-environment (e.g., percentage of detached housing) and wider geographical characteristics (such as aggregate population density) influencing the amount of PV deployed.…”