A bibliometric review of grid parity, energy transition and electricity cost research for sustainable development

John, Temitope M.; Misra, Sanjay; Maskeliūnas, Rytis; Damaševičius, Robertas

doi:10.1016/j.heliyon.2023.e15532

Cited by 7 publications

(9 citation statements)

References 123 publications

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“…Economic analysis has begun to seriously consider environmental implications, moving beyond the traditional growth-oriented models. This section focuses on how sustainable development goals (SDGs) have become integral components of economic analysis, shaping the discourse around green economy, green computing [41], energy transition [42], circular economy [43], and ecological economics. The use of AI methods in evaluating SDGs has become crucial [44,45].…”

Section: Incorporating Sustainability In Economic Modelingmentioning

confidence: 99%

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Damasevicius

2023

FEL

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This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.  In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

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Section: Incorporating Sustainability In Economic Modelingmentioning

confidence: 99%

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Damasevicius

2023

FEL

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“…In the era of digitization, regional economies produce a colossal amount of data on a regular basis. This includes data related to energy transition [28], economic productivity, industry dynamics, employment levels, income distribution, consumer behavior, trade patterns, and much more. Traditional statistical methods are often inadequate for processing and analyzing such massive and diverse datasets.…”

Section: Big Data Processing and Analysismentioning

confidence: 99%

Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges

Damaševičius

2023

JRE

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The dawn of the Artificial Intelligence (AI) era presents a plethora of new possibilities for analyzing regional economic development. The present article provides an in-depth exploration of the methods employed in this field, highlighting the immense opportunities that AI offers while also addressing potential challenges. The role of AI is crucial in complex data handling, enabling efficient analyses of intricate regional economic patterns. This capacity is paramount in shaping economic policies and strategies that are reflective of each region's unique needs and potential. The article firstly explores various AI methods used in economic analysis, including but not limited to machine learning, deep learning, and natural language processing. It delves into the application of these methods in discerning development trends, predicting economic shifts, and identifying strategic economic drivers unique to various regions. Subsequently, the potential of AI to transform regional economic analysis is discussed, encompassing its capability to process large and complex datasets, its power to predict future trends based on past and present data, and its ability to aid in strategic decision-making. However, this new era of AI-driven economic analysis is not without challenges. The latter part of this article thus confronts the issues related to data privacy, ethical use of AI, and the necessity of interdisciplinary skills in AI and economics. This exploration contributes to a broader understanding of how AI is transforming the landscape of regional economic development analysis, illuminating both its present use and future implications. By understanding these dynamics, we can better harness the potential of AI to advance economic prosperity in various regions around the globe.

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“…While previous studies have primarily focused on analyzing past archival data to assess the impact of the adopted FiT system (e.g., Dong et al, 2021;Wang et al, 2016), there has been a limited amount of research on projecting the required amount of subsidies in a specific country's context. It is recommended that we take into account the specific circumstances of each country when simulating the FiT system (Adeyemi-Kayode et al, 2023;Ayoub & Yuji, 2012). Building upon this, our study contributes to simulating the necessary government subsidies in the specific context of Korea, which is characterized by high land costs and a heavy reliance on inexpensive nuclear energy.…”

Section: Introductionmentioning

confidence: 99%

Subsidizing the Shift to Renewable Energy in Korea: A Levelized Cost Analysis and Computation of Required Subsidies

Lee,

Jung

2024

Sage Open

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This study addresses the pivotal challenge of transitioning from nuclear to renewable energy sources, considering the distinctive energy landscape of South Korea characterized by high land costs and high dependence on nuclear energy. Amidst global momentum toward sustainable and carbon-neutral energy, South Korea’s Renewable Energy 3020 Implementation Plan aims to achieve 20% of power generation from renewables by 2030. Shifting away from nuclear energy, however, may lead to rising energy costs, supply disruptions, and environmental ramifications. Furthermore, achieving grid parity between renewable energy and nuclear power by the late 2030s is unlikely, in contrast to earlier projections. This is primarily due to the comparatively lower production cost of nuclear energy. To expedite the shift toward renewable energy, our study suggests transitioning from the Renewable Energy Portfolio Standard (RPS) to a Feed-in-Tariff (FiT) system. This change would offer voluntary incentives for investment in renewable energy. Additionally, we compute Feed-in-Tariff subsidies required to achieve grid parity by 2030, concluding that the amount of subsidies required to reach grid parity by 2030 is 54% of retail electricity price. This research offers valuable insights for policy formulation and energy transition planning.

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A bibliometric review of grid parity, energy transition and electricity cost research for sustainable development

Cited by 7 publications

References 123 publications

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges

Subsidizing the Shift to Renewable Energy in Korea: A Levelized Cost Analysis and Computation of Required Subsidies

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