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Abstract

When residential rooftop solar photovoltaic (PV) systems are widely accepted across society, the uptake of home battery energy storage systems is closely tied to the PV-status quo and the behaviour previously taken by households. This study proposes that a decision of acceptance or rejection of PV systems is the past behaviour of the battery adoption decision. This antecedent role of PV behaviour may spark two attitudinal changes: (a) feelings of regret, which may occur among PV adopters, stemming from a positive experience of using the system, or among non-adopters due to their rejection of the system, and (b) feelings of despair, which may arise among current PV users due to dissatisfaction and discontent with the system. While regret positively changes consumers’ attitude towards battery adoption expediting an earlier purchase, despair could preclude battery installation. Through a survey of 557 households in South East Queensland, Australia, this study investigated factors driving attitude change. Instead of traditional statistical methods, machine learning algorithms were adopted to derive data-driven models of attitude change, allowing for higher prediction accuracy and a determination of the latent causalities. The main findings indicate that perceived attitudes towards financial and non-financial benefits, followed by informal peers, best estimate the attitude change, whereas traditional sociodemographic factors, knowledge and affordability may not engender a shift. Leveraging from this new paradigm can encourage current PV consumers to take another step and become earlier battery adopters. Failure to recognise these dynamics may breed despair and turn current innovators and early adopters of PVs into late adopters or rejectors of battery systems.

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Understanding complex phenomena such as energy transitions, which bear technical, economic and social dimensions, requires a multi-directional approach. Expansion of the solar energy in the energy mix of a country is similarly complex, as its decentralized nature brings about a necessity of public approval and trust besides its economics. We therefore develop a combined socio-economic model, which is based on the sociodynamics framework, for the household-level adoption of photovoltaics (PV). We apply the model to the cases of German and Italian PV expansion and make a retrospective analysis regarding their dynamics, in order to identify the importance of various factors such as the profitability and the public opinion throughout their expansion timeline. We then project our model for the German PV expansion onto the near future and investigate the requirement of feed-in tariffs to maintain the expansion targets under various scenarios. Results of our projection point at the importance of the self-consumption of PV electricity; an average self-consumption ratio higher than 25% makes a phase-out of feed-in tariffs by 2030 possible, whereas 50% self consumption renders the feed-in tariff regulation obsolete even in today's economic conditions.
Article
Building upon recent literature, we combine a novel spatiotemporal variable with spatial methods to investigate and quantify the influence of the built environment and jurisdictional boundaries on spatial peer-effects (SPEs) in inner-city areas. We focus on the Hartford Capital region, using detailed data at block-group and PV system levels for the years 2005-2013. This region is part of a state, Connecticut, actively engaged in supporting PV system at residential level. Adoption of PV systems varies substantially, and state policies are mediated by town-level regulations. We initially employ typology analysis to investigate the heterogeneity of the block groups with higher adoption rates. We then use panel FE and spatial estimations to determine the existence of spill-overs of SPEs beyond town boundaries. Our estimations suggest that new PV systems have a more limited spatiotemporal influence in inner-cities. We identify spatial spill-overs from neighboring block groups even between towns, suggesting that SPEs transcend municipal barriers. We do not find significant results for built-environment, although we identify several data limitations. Our results suggest that centralized, non-voluntary support policies may have larger effects if implemented beyond town-level, and that SPEs change their determination power depending on the underlying built environment. Highlights • We build upon previous spatial peer-effect (SPEs) theory and empirical research. • We focus on am urban environment (BE) with strict jurisdictional boundaries (JB). • We combine spatial & Panel models to verify the influence of BE and JB on SPEs. • We determine JBs do not curb spatial peer effects. • The BE reduces the spatial and temporal effects of spatial peer effects.
Article
The present study focuses on consumer behaviour towards decision-making about residential photovoltaic (PV) technology and motivates them to adopt renewable energy sources in place of conventional resources. The study focuses on the six significant factors of customer attribute related to environmental concern which stimulate the purchase behaviour to adopt residential PV technology. Therefore, the study considers various factors of environmental concerns, retrieved from a previous literature review by the researchers. For obtaining the results, structural equation modelling (SEM) has been utilized to examine the 269 customers’ data, collected by the researchers. The result indicates that the environmental concern factors such as social influence, environmental attitude, environmental knowledge, environmental responsibility and government initiative have significant positive influence on customer intention to adopt residential PV technology. However, the factor awareness of environmental problem shows insignificant influence on intention to adopt. Thus, the outcome of the study will provide some valuable insights to the policymakers, marketers and government for further expansion of solar energy market by using various promotional programmes and strategies, consciousness and sharing responsibility towards saving our environment from detrimental effects of conventional energy resources.
Conference Paper
Topological Data Analysis is a novel approach, useful whenever data can be described by topological structures such as graphs. The aim of this paper is to investigate whether such tool can be used in order to define a set of descriptors useful for pattern recognition and machine learning tasks. Specifically, we consider a supervised learning problem with the final goal of predicting proteins' physiological function starting from their respective residue contact network. Indeed, folded proteins can effectively be described by graphs, making them a useful case-study for assessing Topological Data Analysis effectiveness concerning pattern recognition tasks. Experiments conducted on a subset of the Escherichia coli proteome using two different classification systems show that descriptors derived from Topological Data Analysis-namely, the Betti numbers sequence-lead to classification performances comparable with descriptors derived from widely-known centrality measures, as concerns the protein function prediction problem. Further benchmarking tests suggest the presence of some information despite the heavy compression intrinsic to the protein-to-Betti numbers casting.
Article
In the last decade, feed-in tariffs have been the method of choice for policymakers trying to accelerate the diffusion of solar photovoltaics (PV). Despite the overall effectiveness of feed-in tariffs, actual adoption rates have shown surprising regional differences, pointing to the presence of peer influence and regional spillover effects. For future diffusion of photovoltaics, understanding these social influences on the decision to adopt is key. Several studies have used revealed preference approaches to discern peer effects in PV adoption, proving their existence but leaving open questions about underlying psychological mechanisms. We close this gap by conducting a survey among potential PV adopters in one of the top three fastest-growing European solar markets and find that two types of social norms, descriptive and injunctive norms and their underlying interplay, play an important role in explaining PV adoption decision and diffusion patterns. Our findings have significant policy implications – as an alternative to following the shotgun approach of uniform nationwide incentives, policymakers should consider inducing snowball effects by facilitating the creation of regional hot spots. Such programs, which may be supported through co-investments between federal and local authorities, would effectively complement existing policy approaches.
Article
Innovation is one of the most important drivers of economic development. Even in developing countries, households have access to a wide array of new technologies. However, factors affecting households’ technology adoption decisions remain poorly understood. Using data on solar microgrid adoption from rural India, we investigate the determinants of household technology adoption. We offer all households identical solar products to avoid bias from product differentiation. Households pay a monthly fee for technology use, allowing us to abstract away from credit constraints as a barrier to adoption. The results show that household expenditures and savings as well as the household head’s entrepreneurial attitude are strong predictors of adoption. In contrast, past fuel expenditures, risk acceptance, and community trust are not associated with technology adoption decisions. These findings suggest new directions for research on the microeconomics of household technology adoption, which is critical for sustainable development among the poor in developing countries.
Article
Solar power (i.e., solar photovoltaic) accounts for about 0.3% of total electricity production in Canada. To enhance this contribution to energy supply from solar power, financial incentives and technological breakthroughs alone may not guarantee change. Drawing on a national survey of 2065 Canadian residents, we identify the determinants of technology adoption intention with the exemplary case of rooftop solar. Using a combination of latent and observed variables within a non-linear structural equation model, our analysis quantifies how a set of individual and community level factors affect adoption intention. Analysis reveals that the visibility of solar technology has a particularly strong effect on intention, lending support to social learning and social network theories of diffusion of innovation. Our findings also show that the perceived knowledge of energy systems and being publicly engaged in energy issues significantly increases adoption intention. These conclusions encourage policy options that enhance public engagement and the visibility of solar technology within neighborhoods and communities.
Article
Photovoltaic (PV), as a viable option for renewable energy, has significant potential in Nigeria to provide the desired sustainable energy needs. However, among many of the major barriers faced in its penetration into effective implementation is awareness and information gap. In contributing to alleviating such gaps as they vary across locations, the awareness and information on PV penetration in Nigeria has been studied. The objectives are to present contemporary information and statistics on the awareness of solar PV energy, the attitude towards utilizing PV resources and the expected benefits from PV energy resources using the Likert-scaled questionnaires as the primary data source. The reliability of the latent scales has been tested using Cronbach's alpha whereas the responses to the scale items have been analyzed using descriptive statistics. The results present pointers in remediating PV energy challenges in Nigeria and are vital inputs to energy infrastructure planning, renewable energy investments, and national policy.
Article
The market diffusion of self-consumption technologies, such as photovoltaic and battery systems, is an important aspect in the transition towards a sustainable energy system. Most studies, which address this issue, focus solely on economic aspects and neglect the influence of individual electricity consumption behaviour and consumer preferences on the individual benefit of a self-consumption system. Yet, preferences and behaviour have a significant impact in the market uptake of new technologies, as can be seen in the current sales figures of batteries for the purpose of self-consumption enhancement. The technology is purchased, even though it is still far from economically viable. In this study, a market diffusion model is proposed that is based on 415 individual electricity load profiles, which define the homeowners' consumption behaviour. Additionally, the results of a market survey are included to explicitly model different user groups and map their varying willingness to pay. The results show that homeowners who are likely to adopt a self-consumption technology are on average characterised by higher annual electricity consumption. The consumption behaviour, and therefore the load profiles are heterogeneous within each user group and therefore, also the individual utility of a battery for self-consumption enhancement differs significantly. The results show that the suggested modelling approach is able to explain the past development of battery installations in private households in Germany. Up until 2030, the model simulations suggest a moderate development of battery enhanced self-consumption, resulting in a battery of 2. GW. h in private households in 2030.
This study develops and validates a causal relationship model of the influence of consumer innovativeness, environmental value and marketing on consumers’ intention to install solar power system. A sample of 400 consumers who live in Bangkok was selected using the multistage random sampling method. Structured questionnaires were administered across the sample population to elicit data and structural equation modelling is used to analyse the data. The results indicate that the hypothetical model is consistent with empirical data. Goodness of fit statistics were chi-square=83.070, degree of freedom (df)=70, P-value=0.136, relative chisquare= 1.187, goodness of fit index (GFI)=0.970, comparative fit index (CFI)=1.000, and root mean square error of approximation (RMSEA)=0.022. The three exogenous variables in the hypothetical model accounted for 81 per cent of total variance of consumers’ intention to install solar power system.
Article
Neighbourhood peer effects (social influence) in the diffusion of residential solar photovoltaics (PV) have previously been identified and quantified in a number of studies. Yet, little has been known about the inner workings of peer effects in PV diffusion. In the present work, a survey and interviews were used to study peer effects among Swedish PV adopters. Participants acknowledged peer effects as important for their adoption decision, although they had in general been seriously contemplating PV adoption before the effects. The main function of peer effects appears to have been a confirmation that PV works as intended and without hassle, rather than the procreation of unexpected insights or the provision of more advanced information. Peer effects had mainly occurred through existing and rather close social relationships, rather than between neighbours that did not already know each other. Peer effects appear to have reduced barriers related to PV attributes such as low trialability and low observability of the actual results of adoption. The results suggest that passive peer effects (through seeing PV) were less important than active effects (through direct interpersonal contact), and that seeing PV rarely led to direct contact with adopters, a finding that contrast somewhat to previous literature.
Article
Download for free until 3/16/17: http://bit.ly/solarpvpaper Increased household adoption of solar photovoltaic systems has the potential to reduce greenhouse gas emissions associated with providing electricity. Although residential solar has recently become more affordable, market penetration in the U.S. remains relatively low. This study proposes a theoretical framework for investigating the psychological and social determinants of interest in residential solar drawn from three theories that may explain the decision to pursue it: diffusion of innovations theory, theory of planned behavior, and value-belief-norm theory. We test this framework using survey data from 904 non- adopter homeowners, with the aim of identifying potential levers for intervention. Overall, we find that consumers see solar electricity in multiple ways: as an environmental benefit, a consumer good, and an innovative technology. Notably, individuals who trust installers and believe solar will be personally beneficial are more likely to consider contacting an installer, as are individuals drawn to novel products. Proenvironmental personal norms indirectly increase interest through perceived personal benefits, suggesting that marketing efforts aimed at environmentally-concerned individuals may need to emphasize non-environmental benefits. The results also support leveraging trusted social networks to convey the benefits of solar. We conclude by discussing the value of the integrated framework along with implications for policymakers and marketers.
Article
This paper examines the role of the consumer in the emerging household-level battery market. We use stated preference data and choice modelling to measure household preferences for battery attributes and functionality. Our survey sample has been sourced from the State of Queensland, Australia, which has some of the highest per capita PV installation rates in the world and has many characteristics of an early-adopter market for battery storage. While cost will be a key determinant for mass market uptake, our study found that drivers encouraging self-sufficiency and grid independence will have a strong influence on battery system preferences. A majority of the 268 respondents to our survey would prefer to buy medium or large battery systems despite higher costs and longer payback periods. Nearly 70% of respondents hope to eventually disconnect from the existing centralized electricity supply network. Should these findings translate more broadly, and battery prices decline as forecast, changing energy market dynamics could result in a range of negative outcomes. Declining infrastructure utilization, asset impairment, rising electricity costs and negative social outcomes could eventuate as consumers attempt to reduce their reliance on existing electricity supply systems. To proactively manage these risks, our study demonstrates the clear need to better understand and address consumer motivations in the impending energy market transition.
Article
A generalized form of the cross‐validation criterion is applied to the choice and assessment of prediction using the data‐analytic concept of a prescription. The examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.