Case Study Evidence and Behavioural Analysis of Residential Energy Consumption in the UK

Hallin, Sven; Hooper, Elizabeth A.; Weyman‐Jones, Thomas

doi:10.4236/ojee.2017.61002

Cited by 5 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ideally, this process can identify a series of new intervention opportunities that foster migrant integration for an entire system. For example, the behavioural analyses of antimicrobial resistance (Pinder et al, 2015) and climate change (Hallin et al, 2017) both address problems on a global scale.…”

Section: Using Behaviour Change Toolsmentioning

confidence: 99%

Applying behavioural science to refugee integration

Sanders¹,

Castle²,

Tan³

et al. 2019

Responsibility for Refugee and Migrant Integration

View full text Add to dashboard Cite

Successful refugee and migrant integration has been shown to generate novel opportunities for development, and to enrich countries economically, socially and culturally. Nonetheless integration is one of the most complex issues of our time. Here we review this problem from a behavioural science perspective. Behavioural science brings together insights from psychology, behavioural economics, neuroscience, and sociology to devise and improve population-level interventions and to develop more effective policies. One approach in behavioural science is commonly referred to as 'nudging'. Recently there has been a growing interest in nudge strategies among both practitioners and academics, in part because the strategies are cheap to implement. Here we provide an overview of such strategies and their applicability to refugee integration. By addressing two sectors of society where behavioural science is currently being applied (education and employment), we examine how behavioural evidence may be used to bypass barriers and facilitate drivers of integration. Our review (i) reveals that few interventions aimed at refugee integration use a behavioural science approach, (ii) highlights areas in which this approach could be especially effective, and (iii) identifies some behavioural science techniques that may be counterproductive.

show abstract

Section: Using Behaviour Change Toolsmentioning

confidence: 99%

Applying behavioural science to refugee integration

Sanders¹,

Castle²,

Tan³

et al. 2019

Responsibility for Refugee and Migrant Integration

View full text Add to dashboard Cite

show abstract

“…The UK's target of cutting CO 2 emissions by 80% by 2050 makes it imperative to generate forecasts that can guide policymakers towards achieving the target. 1 Figure 1 presents the UK's energy consumption and temperature trends from January 1995 to March 2022. We observe that electricity consumption has been declining since the early 2010s, while gas consumption has been on the increase since 2016.…”

Section: Introductionmentioning

confidence: 99%

“…This is because the responses of economic agents to external shocks, risks and uncertainty may differ from forecasts predicated on classical rational optimising behaviour. 1 The outcome of this paper would help shape the design and implementation of more effective energy policies. [3][4][5] In addition, energy consumption forecasts can provide crucial signals useful for trading in energy markets.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric impacts of natural gas consumption on renewable energy and economic growth in Kingdom of Saudi Arabia and the United Arab Emirates

Alam

Adebayo

Said

et al. 2022

Energy & Environment

View full text Add to dashboard Cite

The globe is now in ecological turmoil as a result of the unrelenting increase in global warming. As a result, governments worldwide are committing to decarbonizing the environment, with the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) playing an important role in this effort. Hence, this paper evaluates the nonlinear (asymmetric) impact of natural gas consumption on renewable energy consumption and economic growth in the KSA and the UAE utilizing data stretching from 1990 to 2020. The study also considers other drivers of renewable energy consumption and economic growth, such as trade openness and CO2 emissions. The study utilizes nonlinear autoregressive distributed lag (ARDL) to evaluate these associations. The outcomes of bounds nonlinear ARDL (NARDL), affirm the long-run association between the variables in both countries. The nonlinear results show that positive and negative shocks in natural gas consumption have a negative impact on renewable energy in both UAE and KSA. In contrast, positive and negative shocks in natural gas consumption impact economic growth positively. The study proposed vital policy recommendations based on these results.

show abstract

“…The UK's target of cutting CO 2 emissions by 80% by 2050 makes it imperative to generate forecasts that can guide policymakers towards achieving the target. 1…”

Section: Introductionmentioning

confidence: 99%

“…This is because the responses of economic agents to external shocks, risks and uncertainty may differ from forecasts predicated on classical rational optimising behaviour. 1 The outcome of this paper would help shape the design and implementation of more effective energy policies. 3–5 In addition, energy consumption forecasts can provide crucial signals useful for trading in energy markets.…”

Section: Introductionmentioning

confidence: 99%

Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches

Bala¹,

Shuaibu

2022

Energy & Environment

View full text Add to dashboard Cite

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector.

show abstract

Case Study Evidence and Behavioural Analysis of Residential Energy Consumption in the UK

Cited by 5 publications

References 14 publications

Applying behavioural science to refugee integration

Applying behavioural science to refugee integration

Asymmetric impacts of natural gas consumption on renewable energy and economic growth in Kingdom of Saudi Arabia and the United Arab Emirates

Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches

Contact Info

Product

Resources

About