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Due to an increase in energy demands, it has become vital to devise efficient energy policies. Literature has suggested multiple factors influencing the consumption of specific energy types. Among others, institutional quality (INQ) is another factor that can determine energy consumption. Given this, the current study aimed to investigate the impact of INQ on fossil fuel energy (FFE) and renewable energy consumption (REC).

The empirical analysis was conducted on 20 years (2000–2019) of data from South Asian economies, and regression among variables was established by employing the dynamic ordinary least square and fully modified ordinary least square models. The selection of both techniques is subject to the existence of cointegration identified by the Johansen cointegration test. Other pre-estimation techniques include cross-section dependence and unit root testing validating the estimation of coefficients in the long run.

The analysis mainly reveals the negative impact of INQ on FFE and the positive impact of INQ on REC. The authors further find the asymmetric impact of control variables including foreign direct investment inflow, economic growth, inflation rate, financial sector development and energy investment on the consumption of both types of energy.

Given the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically those that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts to improve the INQ.

In view of the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts for improving the INQ.

This study offers robustness to the empirical findings of existing literature on the INQ-REC nexus and complements the underdeveloped literature on the INQ-FFE relationship.

This study examines the temporal trends and determinants of household cooking fuel choices in India. Access to affordable, reliable, clean cooking fuels is crucial for improving household health and mitigating environmental issues. However, a significant portion of the population in developing countries, including India, still relies on traditional solid biomass fuels, leading to adverse health impacts. Using data from India Human Development Survey conducted in 2005 and 2012, this research analyzes changes in fuel choices. It explores the influence of socio-demographic characteristics, education, and accessibility on these choices over time. The findings reveal a gradual transition toward mixed fuel usage but a limited reduction in the use of dirty fuels, indicating the challenges in achieving cleaner cooking practices. Education is a crucial driver of fuel choices, highlighting the need for targeted educational campaigns. Age and household size also play significant roles, with older household heads and larger households exhibiting different fuel preferences. The availability and cost of firewood and kerosene influence fuel choices. The study also suggests developing educational campaigns, improving the availability and affordability of clean cooking fuels, and tailored strategies for larger households. These findings provide guidance for policymakers in promoting the adoption of cleaner cooking fuels and improving household air quality and public health in India.

By comparing and contrasting the two scenarios of power producers investing in renewable energy and electricity sellers investing in renewable energy, we explore the conditions under which profit growth and carbon emission reduction can be realized, and provide a theoretical basis for decision-making on renewable energy investment by electric power companies as well as for government policy formulation.

This paper constructs a game model of a grid supply chain consisting of a leader generator and a follower seller in the context of the C&T mechanism, considering two scenarios in which the generator and the seller invest in renewable energy. Conclusions are drawn by comparing and analyzing the equilibrium solutions in different scenarios.

The scenario where electricity sellers invest in renewable energy exhibits a higher investment volume compared to the scenario involving power generators. In scenarios where power producers invest in renewable energy, electricity sellers achieve lower profits than power generators, while scenarios with electricity seller' investments yield higher profits for them. Increasing the cost coefficient of renewable energy investment reduces investment volume, electricity prices and electricity demand, leading to decreased profits for electricity seller but increased profits for power generator. A rise in the preference coefficient for renewable energy results in increased profits for electricity seller but decreased profits for power generator.

Addressing a literature gap in the context of low carbon, this study examines the investment scenario of electricity sellers in low carbon technologies, complementing existing research focused on power generators and consumers. The findings enrich knowledge in low carbon investment. By analyzing the investment decisions of both power producers and electricity sellers, this study explores the practical implications of renewable energy investments on the decision-making and operational dynamics of power supply chain enterprises. It sheds light on their profitability and investment strategies.

The impact of COVID-19 on the lives of people and businesses across the globe was devastating. While governments across the world had undertaken a slew of measures to control the spread of the COVID-19 virus within their geography, many of these measures had long and unintended consequences. The restrictions imposed by the governments on the movement of people and goods across the world brought supply chains to a grinding halt. This study identifies the cascading effects of supply chain disruptions (SCDs) on the energy sector and thereby on the security of supply of energy from a European Union perspective. Since these systems are closely integrated and the impact of COVID-19 needs to be analysed at a much broader level, this study uses a systems-thinking approach to study the effect of SCDs on energy services. The study develops a causal loop model to gain further insight into how SCDs caused by COVID-19 affected the coping capabilities of society and how critical services were affected. Furthermore, the study puts forth certain policy recommendations for both businesses and governments to prepare for and protect against a similar situation in the future.

Severe inequality from climate change exists between the Global North and Global South. The North significantly contributes to climate change yet retreats to protect itself against its harmful impacts. Conversely, members of the Global South bear the brunt of the climate crisis with limited protection against its destructive effects. Climate justice aims to address this inequality. This paper explores the effects of climate change reforms and policies that have been established to foster accountability and climate justice.

This research follows a qualitative exploratory case study method. It draws on a supply- and demand-led approach and local accounts to analyse the (in)effectiveness with which six national and international reforms and policies have achieved accountability for climate justice. The research analysed a variety of empirical documents including contemporary research, reports, academic literature, non-government and government documents and policies, media releases and Pacific Islander accounts.

Climate change reforms and policies, which come together to form supply-side accountability, have largely failed to engender accountability in the Global North for the impacts of climate change. Nor have they mitigated climate change to any tangible extent at all. This has created a system of modern-day climate apartheid. Improving accountability and remediating climate injustices going forward will require a focus on demand-led instruments and accountability, which includes the voice of citizens.

This paper responds to AAAJ’s special issue call for examining accounting and accountability with regard to environmental and climate racism. Limited research to date explores the issue of climate apartheid and climate justice and its relationship with accountability. This research attempts to fill that gap.

The purpose of this study is to examine crisis effects on customer satisfaction and underlying drivers by adding a new set of data to previous research. The core questions are: are the findings from Hallencreutz and Parmler (2019, 2021) sustained or can new customer demands, needs, expectations and behaviours be traced in the wake of the ongoing crisis?

A first study covering 2005–2017 was completed in 2018, published online in 2019 and in print in 2021 (Hallencreutz and Parmler, 2021). This new study adds the years 2018–2023 to the data set and reuses the partial least squares (PLS) approach to structural equation models, also known as PLS path modelling.

This additional study sustains the results from the initial study (Hallencreutz and Parmler, 2019, 2021). The variable product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction together with brand image, and the current state of permacrisis has not changed that.

The study is built on Swedish data from the EPSI Rating Initiative (Eklöf and Westlund 2002) covering customer perceptions in banking, insurance (life and non-life), telco (mobile operators, broadband and Pay-tv) and energy (trade, distribution and heating) over the years 2005–2023.

The study emphasizes the importance of understanding how customer satisfaction drivers evolve over time in different industries and societal sectors, especially during crises. This additional study sustains the paradigm shift in the studied industries – product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction, and the current state of economic downturn has not changed that.

Society will have to learn to live with political and economic instability and unpredictability for the foreseeable future. To recognize the increasing value deriving from firms’ intangible assets while providing flawless deliveries seems to be a way forward in troublesome times. This is also a catalyst for existing societal trends: the necessary reforms to master sustainable transformations will require an ongoing adaptation process, with both winners and losers across continents.

The world has coped with a global pandemic, and Europe is currently experiencing a humanitarian, political and economic crises caused by a war in Ukraine. This extended period of global instability and insecurity could be called a permacrisis (Collins dictionary, 2022). This study offers a unique quantitative analysis built on Swedish data from EPSI Rating initiative.

The present study aims at quantifying the likely impacts of an environmental tax on macroeconomic variables and pollution in Iran.

The computable general equilibrium model, which allows the prediction of the economy-wide effects of any change in policy instruments, is applied.

The main findings reveal that gross domestic product, private consumption and income in both urban and rural areas will follow a declining trend as a result of environmental tax imposition. In a scenario with the highest level of tax, the predicted percentage change to the gross domestic product and private consumption is estimated at −21.32 and −40.96, respectively. In the same scenario, pollution emissions would decrease by 12.4–22.6% for CO2, CH4 and N2O.

This study uses a general equilibrium model to examine the effects of the carbon tax on environmental issues and household welfare, considering the unique conditions and regulations of Iran. While the related literature examines the CO2 tax, the current study covers more pollutants, including CO2, CH4, N2O, CO, SO2 and NOx. In addition, a distinguishing feature of the current study is that it applies a modified version of the social accounting matrix (SAM) database, which includes the heavy subsidies of energy products. Another significant feature of the current study is that it examines tax policy while tax rates are exerted endogenously (compared to previous studies).

This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.

The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.

The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R², yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.

This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

Sustainable development hinges on a crucial shift to renewable energy, which is essential in the fight against global warming and climate change. This study explores the relationships between artificial intelligence (AI), fuel, green stocks, geopolitical risk, and Ethereum energy consumption (ETH) in an era of rapid technological advancement and growing environmental concerns.

This research stands at the forefront of interdisciplinary research and forges a path toward a comprehensive understanding of the intricate dynamics governing green sustainability investments. These objectives have been fulfilled by implementing the innovative quantile time-frequency connectedness approach in conjunction with geopolitical and climate considerations.

Our findings highlight coal market dominance and Ethereum energy consumption as critical short- and long-term market volatility sources. Additionally, geopolitical risks and Ethereum energy consumption significantly contribute to volatility. Long-term factors are the primary drivers of directional volatility spillover, impacting green stocks and energy assets over extended periods. Additionally, SHapley Additive exPlanations (SHAP) findings corroborate the quantile time-frequency connectedness outcomes.

This study highlights the critical importance of transitioning to sustainable energy sources and embracing digital finance in fostering green sustainability investments, illuminating their roles in shaping market dynamics, influencing geopolitics and ensuring the long-term sustainability required to combat climate change effectively.

The study offers practical sustainability implications by informing green investment choices, strengthening risk management strategies, encouraging interdisciplinary cooperation and fostering digital finance innovations to promote sustainable practices.

The implementation of the quantile time-frequency connectedness approach, in line with considering geopolitical and climate factors, marks the originality of this paper. This approach allows for a dynamic analysis of connectedness across different distribution quantiles, providing a deeper understanding of variable interactions under varying market conditions.