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The present time is a seminal decade for the transition of the energy sector through the deployment of green energy and the optimization of efficiencies using the power of automation and artificial intelligence (AI), which demands competitive policies to handle multidimensional endeavors via a single platform. The failure of energy policies can have far-reaching socioeconomic consequences when policies do not meet the energy and climate goals throughout the lifecycle of the policy. Such shortcomings are reported to be due to inadequate incentives and poor decision making that needs to promote fairness, equality, equity, and inclusiveness in energy policies and project decision making. The integration of AI in energy sectors poses various challenges that this study aims to analyze through a comprehensive examination of energy policy processes. The study focuses on (1) the decision-making process during the development stage, (2) the implementation management process for the execution stage, (3) the integration of data science, machine learning, and deep learning in energy systems, and (4) the requirements of energy systems in the context of substantiality. Synergistically, an emerging blueprint of policy, data science and AI, engineering practices, management process, business models, and social approaches that provides a multilateral design and implementation reference is propounded. Finally, a novel framework is developed to develop and implement modern energy policies that minimize risks, promote successful implementation, and advance society’s journey towards net zero and carbon neutral objectives.

In this article, I quantify the extent to which lobbying expenditures by firms affect policy enactment. To achieve this end, I construct a novel dataset containing all federal energy legislation and lobbying activities by the energy sector during the 110th Congress. I then develop and estimate a game-theoretic model where heterogeneous players choose lobbying expenditures to affect the probability that a policy is enacted. I find that the effect of lobbying expenditures on a policy's equilibrium enactment probability to be statistically significant but very small. Nonetheless, the average returns from lobbying expenditures are estimated to be over 130%.

This contribution offers a thorough analysis of challenges and opportunities related to the adoption of sustainable energy policies in specific developing countries (i.e., Albania, Brazil, India, Kenya). The use of renewable energy sources must be increased if the world is to meet its climate goals and alleviate the negative effects of fossil fuel consumption. However, due to fiscal restrictions, institutional barriers, and technology limitations, developing countries face particular challenges in adopting such policies. In order to help these countries move towards a sustainable energy future, this study analyses these issues and suggests viable solutions for policymakers.

The COVID-19 pandemic and Russia’s war on Ukraine have impacted the global economy, including the energy sector. The pandemic caused drastic fluctuations in energy demand, oil price shocks, disruptions in energy supply chains, and hampered energy investments, while the war left the world with energy price hikes and energy security challenges. The long-term impacts of these crises on low-carbon energy transitions and mitigation of climate change are still uncertain but are slowly emerging. This paper analyzes the impacts throughout the energy system, including upstream fuel supply, renewable energy investments, demand for energy services, and implications for energy equity, by reviewing recent studies and consulting experts in the field. We find that both crises initially appeared as opportunities for low-carbon energy transitions: the pandemic by showing the extent of lifestyle and behavioral change in a short period and the role of science-based policy advice, and the war by highlighting the need for greater energy diversification and reliance on local, renewable energy sources. However, the early evidence suggests that policymaking worldwide is focused on short-term, seemingly quicker solutions, such as supporting the incumbent energy industry in the post-pandemic era to save the economy and looking for new fossil fuel supply routes for enhancing energy security following the war. As such, the fossil fuel industry may emerge even stronger after these energy crises creating new lock-ins. This implies that the public sentiment against dependency on fossil fuels may end as a lost opportunity to translate into actions toward climate-friendly energy transitions, without ambitious plans for phasing out such fuels altogether. We propose policy recommendations to overcome these challenges toward achieving resilient and sustainable energy systems, mostly driven by energy services.

This study presents an innovative framework for leveraging the potential of AI in energy systems through a multidimensional approach. Despite the increasing importance of sustainable energy systems in addressing global climate change, comprehensive frameworks for effectively integrating artificial intelligence (AI) and machine learning (ML) techniques into these systems are lacking. The challenge is to develop an innovative, multidimensional approach that evaluates the feasibility of integrating AI and ML into the energy landscape, to identify the most promising AI and ML techniques for energy systems, and to provide actionable insights for performance enhancements while remaining accessible to a varied audience across disciplines. This study also covers the domains where AI can augment contemporary and future energy systems. It also offers a novel framework without echoing established literature by employing a flexible and multicriteria methodology to rank energy systems based on their AI integration prospects. The research also delineates AI integration processes and technique categorizations for energy systems. The findings provide insight into attainable performance enhancements through AI integration and underscore the most promising AI and ML techniques for energy systems via a pioneering framework. This interdisciplinary research connects AI applications in energy and addresses a varied audience through an accessible methodology.

This study aims to identify the most significant sustainable energy policies by considering quality function deployment (QFD) perspectives, presenting the most appropriate energy policy with a comprehensive analysis. For this purpose, a comprehensive evaluation is conducted by combining DEMATEL and TOPSIS with Quantum Spherical fuzzy sets (QUSEFs) and golden cut: (1) the significance levels of the Quintuple innovation dimensions for planning counterparties are weighted; (2) the relative roles of the innovation counterparties in the Quintuple innovation dimensions are examined; (3) the marketing priorities of innovation counterparties are created; (4) the sustainability of innovative marketing tools is ranked; (5) significant sustainable energy policies are generated. It is concluded that technological issues play a crucial role in generating effective energy policies. The government is also determined to be crucial in generating clean energy policies. Governments have two responsibilities: must create the necessary legal arrangements for these policies, while the financial support governments will provide to investors is about to have a vital role in this process. The findings indicate that using smart systems to increase active participation is the most critical clean energy investment policy. This implementation contributes to increasing energy investment efficiency so that clean energy can become more widespread.

Responses to climate change are strongly linked to political identity and therefore any efforts to promote climate change policy need to take political identity into account. In the current research, we developed communication strategies, informed by the social identity approach, that promoted climate change policies to Republicans and Democrats. In experiment 1 (N = 879), we presented messages to Republican and Democrat participants about a carbon tax policy that differed in terms of whether the policy was endorsed by members of the Republican or Democrat party, and whether the policy was promoted on the basis of Republican or Democrat values. Experiment 2 (N = 1008) adopted the same design but the focus was on a nuclear energy policy. Across both studies, participants had more positive responses—more favorable attitudes, greater support, and stronger intentions to engage in policy-supportive behavior—when the climate change policy was endorsed by members of their ingroup than the outgroup. In experiment 1, Democrat participants (but not Republican participants) also had more positive attitudes to the carbon tax policy when it was framed in a way that aligned with the values of their ingroup. In experiment 2, Democrat participants again had more positive responses to the nuclear energy policy when it was promoted on the basis of ingroup values, whereas values did not influence Republican participants. These findings demonstrate the importance of considering social identity motivations when communicating about climate change policies.

States have historically been the primary drivers of climate change policy in the US, particularly with regard to emissions from power plants. States have implemented policies designed either to directly curb greenhouse gas (GHG) emissions from power plants, or to encourage energy efficiency and renewable energy growth. With the federal government withdrawing from the global climate agreement, understanding which state-level policies have successfully mitigated power-plant emissions is urgent. Past research has assessed policy effectiveness using data for periods before the adoption of many policies. We assess 17 policies using the latest data on state-level power-sector CO 2 emissions. We find that policies with mandatory compliance are reducing power-plant emissions, while voluntary policies are not. Electric decoupling, mandatory GHG registry/reporting and public benefit funds are associated with the largest reduction in emissions. Mandatory GHG registry/reporting and public benefit funds are also associated with a large reduction in emissions intensity. State policies play a key role in mitigation of power-sector emissions. Analysis of 17 policies in the US shows that mandatory compliance is reducing emissions, with the largest reductions related to greenhouse gas reporting and public benefit funds.

Retrospective voting studies typically examine policies where the public has common interests. By contrast, climate policy has broad public support but concentrated opposition in communities where costs are imposed. This spatial distribution of weak supporters and strong local opponents mirrors opposition to other policies with diffuse public benefits and concentrated local costs. I use a natural experiment to investigate whether citizens living in proximity to wind energy projects retrospectively punished an incumbent government because of its climate policy. Using both fixed effects and instrumental variable estimators, I identify electoral losses for the incumbent party ranging from 4 to 10%, with the effect persisting 3 km from wind turbines. There is also evidence that voters are informed, only punishing the government responsible for the policy. I conclude that the spatial distribution of citizens' policy preferences can affect democratic accountability through 'spatially distorted signalling', which can exacerbate political barriers to addressing climate change.