Harnessing ESG Sustainability, Climate Policy Uncertainty and Information and Communication Technology for Energy Transition
Abstract
1. Introduction
- What is the effect of ESG sustainability on energy transition?
- How does ICT impact energy transition?
- Does financial development influence energy transition?
- What role does climate policy uncertainty play in fostering energy transition?
Novelty of this Study
2. Theoretical Underpinning and Empirical Review
2.1. Theoretical Underpinning
2.2. Empirical Review
2.3. Gap in the Literature
3. Data and Methods
3.1. Data
3.2. Method
4. Results of Analysis
4.1. Descriptive Statistics
4.2. Quantile Cointegration
4.3. Quantile ARDL Results
4.3.1. Short-Term Effects (δ Coefficients)
4.3.2. Long-Term Effects (φ Coefficients)
4.3.3. Adjustment Coefficient (ρ)
4.3.4. Speed of Adjustment (ζ)
4.4. Quantile Granger Causality
4.5. Discussion of Findings
5. Conclusions and Policy Recommendations
5.1. Conclusions
5.2. Policy Recommendations
- (a)
- Financial Development (FD): To capitalize on FD’s positive impact on ET at lower quantiles, policymakers should prioritize enhancing financial incentives and regulatory frameworks that promote green investments. This includes expanding tax credits for renewable energy projects, incentivizing green bonds, and fostering partnerships between financial institutions and clean energy developers. By facilitating easier access to capital for sustainable infrastructure, such policies can accelerate the deployment of renewable technologies, particularly in underserved communities and rural areas.
- (b)
- ICT (Information and Communication Technology): Recognizing ICT’s initial negative impact on ET in lower quantiles, policies should focus on balancing digital innovation with environmental sustainability. This can be achieved by promoting energy-efficient ICT solutions, such as smart grid technologies, AI-driven energy management systems, and incentivizing data centers to adopt renewable energy sources. Additionally, regulatory measures can be implemented to ensure that ICT infrastructure investments align with long-term sustainability goals, minimizing the short-term energy consumption drawbacks associated with digital expansion.
- (c)
- Climate Policy Uncertainty (CPU): Given CPU’s disruptive impact across quantiles, policy responses should focus on enhancing the stability and credibility of the U.S. climate policy framework. This involves establishing consistent and long-term regulatory signals, institutionalizing climate targets through legislation, and designing adaptive policy instruments that can withstand political and economic cycles. By reducing ambiguity around future climate regulations, tax incentives, and renewable energy mandates, such measures can strengthen investor confidence and reduce the risk premium associated with clean energy investments. Furthermore, fostering domestic energy security through expanded deployment of renewable resources, supporting innovation in green technologies, and enhancing international cooperation on climate governance can insulate the U.S. energy sector from policy-induced volatility. A stable and predictable climate policy environment is essential for mobilizing sustained investments and accelerating the clean energy transition.
- (d)
- ESG Integration: To address the short-term costs of ESG compliance and maximize its long-term benefits, policymakers should promote regulatory clarity and support mechanisms for firms transitioning towards sustainable practices. This includes incentivizing ESG reporting through tax incentives, promoting transparency in environmental audits, and fostering public-private partnerships to facilitate ESG integration across industries. By aligning financial incentives with environmental and governance standards, policies can foster a business environment where ESG considerations drive innovation and long-term value creation.
- (a)
- Financial Development (FD): Building on its foundational role, long-term policies should focus on expanding green finance initiatives and strengthening institutional support for sustainable investments. This includes developing standardized green finance guidelines, supporting the growth of green bonds and investment funds, and integrating climate risk assessments into financial decision-making processes. By fostering a robust green finance ecosystem, policymakers can ensure continued capital flows into renewable energy projects, supporting ET goals across all quantiles.
- (b)
- ICT (Information and Communication Technology): To harness ICT’s potential as a long-term enabler of ET, policies should promote the deployment of advanced digital infrastructure that enhances energy efficiency and integrates renewable energy sources. This involves investing in smart grid technologies, accelerating the adoption of AI-driven energy management systems, and incentivizing ICT innovations that support environmental sustainability. Regulatory frameworks should incentivize the use of ICT for optimizing energy consumption patterns and promoting grid modernization, ensuring that digital advancements contribute positively to long-term ET objectives.
- (c)
- Climate Policy Uncertainty (CPU): Long-term strategies should prioritize reducing climate policy uncertainty by establishing consistent, transparent, and forward-looking regulatory frameworks that support clean energy development. This includes enacting stable climate legislation, setting clear decarbonization targets, and institutionalizing long-term incentives for renewable energy investments. Such measures can reduce ambiguity for investors and firms, lower risk premiums, and encourage sustained capital flows into green infrastructure. Additionally, promoting energy independence through diversified local renewable resources, enhancing grid resilience via advanced energy storage systems, and strengthening international cooperation on climate and energy governance can help insulate the U.S. energy system from external shocks. By minimizing the destabilizing effects of climate policy uncertainty, these efforts will foster a more predictable policy environment that facilitates long-term planning, drives innovation, and accelerates the transition to a low-carbon economy.
- (d)
- ESG Integration: As ESG frameworks evolve, long-term policies should continue to support firms in adopting sustainable business practices and aligning with global sustainability standards. This involves implementing stringent climate risk disclosure requirements, fostering industry collaboration on ESG best practices, and incentivizing green innovation through research grants and tax incentives. By embedding ESG considerations into corporate governance and investment decisions, policymakers can enhance the long-term competitiveness of U.S. firms, drive innovation in clean technologies, and accelerate progress towards achieving ET goals across quantiles.
5.3. Limitation and Future Directions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author(s) | Period(s) | Nation(s) | Method(s) | Finding(s) |
---|---|---|---|---|
Impact of Financial Development on Energy Transition | ||||
[9] | 1994–2015 | 34 upper-middle-income developing countries | Panel cointegration, FMOLS | FD ↑ ET |
[11] | 1971–2015 | India | Maki cointegration, DOLS, VECM | FD ↑ ET |
[26] | 1990–2018 | Ghana | ARDL, FMOLS, VECM | FD ↑↓ ET |
[27] | 1990–2019 | ASEAN + 3 countries | Panel ARDL | FD ↑↓ ET |
[25] | 1991–2012 | G20, OECD, EU countries | FMOLS, STIRPAT model | FD ↑ ET |
Impact of ICT on Energy Transition | ||||
[28] | 2001–2020 | Africa (panel, ~29 countries) | PARDL | ICT ↑ ET |
[16] | 1996–2021 | Denmark | regression | ICT → ET |
[29] | 2000–2019 | 126 countries (global) | Panel regression | ICT ↑ ET |
[30] | Not Defined | Multinational studies | Review | ICT ↑ ET |
Impact of Climate Policy Uncertainty on Energy Transition | ||||
[31] | 2000–2022 | United States | ARDL/FMOLS/DOLS | CPU ↑ ET |
[19] | Not Defined | United States | Non-linear threshold AR model | CPU ↓ ET |
[32] | 2013–2022 | Canada | Wavelet Power Spectrum & Coherence | CPU ↑ ET |
[20] | 1987–2024 | United States | Granger causality | CPU ↑ ET |
[21] | 1989–2023 | USA | Rolling Window | CPU ↑ ET |
[33] | varies | developed countries | Panel regression | CPU ↑ ET |
Impact of ESG Sustainability on Energy Transition | ||||
[35] | 2011–2024 | China (traditional energy firms) | Zero-inflated negative binomial model | ESG ↑ ET |
[24] | 2013–2022 | China (provinces) | Threshold regression/fixed effects | ESG ↑↓ ET |
[23] | 2005–2020 | OECD (10 countries) | Panel regression | ESG ↑ ET |
[34] | 2000–2021 | EU-10 nations | VECM & FMOLS | ESG ↔ ET |
[15] | – | 10 countries | Machine learning forecasting | ESG ↑ ET |
Sign | Variables | Measurement | Sources |
---|---|---|---|
CPU | Climate Policy Uncertainty | Index | [36] |
ESG | Environmental Social Governance Sustainability | Index | [36] |
ET | Energy Transition | (Trillion Btu) | [1] |
FD | Financial Development | Domestic credit to private sector by banks (% of GDP) | [37] |
ICT | Information and Communication Technology | Individuals using the Internet (% of population) | [37] |
Statistic | CPU | ESG | ET | FD | ICT |
---|---|---|---|---|---|
Minimum | 3.896 | 2.512 | 5.759 | 3.802 | 4.091 |
Maximum | 6.275 | 4.357 | 6.61 | 4.101 | 4.547 |
Mean | 4.782 | 3.253 | 6.245 | 3.955 | 4.351 |
Median | 4.701 | 3.252 | 6.322 | 3.947 | 4.311 |
Stdev | 0.501 | 0.375 | 0.247 | 0.061 | 0.135 |
Skewness | 0.485 | 0.26 | −0.539 | 0.567 | 0.017 |
Kurtosis | 2.534 | 2.708 | 1.989 | 3.443 | 1.713 |
Jarque-Bera | 4.339 * | 1.334 | 8.187 *** | 5.555 *** | 6.213 *** |
Quantiles | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.05 | Coefficient | −0.002 | −0.218 *** | 0.357 | −0.072 | −0.01 * | −0.169 | 0.896 * | 0.260 *** | −0.017 ** | 0.002 | 0.005 |
SE | 0.004 | 0.047 | 0.238 | 0.039 | 0.018 | 0.097 | 0.185 | 0.158 | 0.008 | 0.007 | 0.043 | |
0.10 | Coefficient | −0.005 ** | −0.056 * | 0.443 ** | −0.049 * | −0.031 *** | −0.240 *** | 0.835 *** | 0.185 ** | −0.005 * | 0.001 | 0.016 |
SE | 0.002 | 0.032 | 0.172 | 0.029 | 0.015 | 0.072 | 0.113 | 0.096 | 0.005 | 0.004 | 0.027 | |
0.20 | Coefficient | −0.004 ** | −0.043 | 0.487 *** | −0.053 * | −0.036 ** | −0.264 *** | 0.828 *** | 0.152 * | −0.007 ** | 0.000 | 0.035 * |
SE | 0.002 | 0.028 | 0.135 | 0.03 | 0.018 | 0.050 | 0.100 | 0.085 | 0.004 | 0.004 | 0.024 | |
0.30 | Coefficient | −0.003 | −0.020 ** | 0.499 | −0.047 *** | −0.009 | −0.270 | 0.824 *** | 0.141 *** | −0.002 * | −0.002 | 0.034 * |
SE | 0.001 | 0.026 | 0.14 | 0.032 | 0.022 | 0.044 | 0.093 | 0.079 | 0.004 | 0.004 | 0.022 | |
0.40 | Coefficient | −0.001 | −0.016 | 0.470 | −0.041 *** | 0.010 | −0.254 | 0.818 *** | 0.119 *** | −0.001 ** | −0.001 | 0.004 |
SE | 0.001 | 0.024 | 0.145 | 0.035 | 0.022 | 0.058 | 0.09 | 0.076 | 0.004 | 0.004 | 0.021 | |
0.50 | Coefficient | 0.000 | −0.032 | 0.320 | 0.003 ** | 0.005 | −0.302 *** | 0.745 *** | 0.098 *** | 0.000 * | −0.001 | −0.004 *** |
SE | 0.001 | 0.025 | 0.126 | 0.026 | 0.021 | 0.067 | 0.087 | 0.075 | 0.004 | 0.003 | 0.021 | |
0.60 | Coefficient | 0.000 | −0.02 | 0.419 | 0.010 *** | −0.013 | −0.381 ** | 0.739 *** | 0.062 *** | 0.001 | 0.001 | −0.006 |
SE | 0.001 | 0.026 | 0.081 | 0.017 | 0.016 | 0.057 | 0.089 | 0.076 | 0.004 | 0.004 | 0.022 | |
0.70 | Coefficient | 0.001 | −0.01 | 0.435 | 0.002 *** | −0.014 | −0.401 * | 0.709 *** | 0.049 *** | 0.000 | −0.001 | 0.001 |
SE | 0.001 | 0.026 | 0.085 | 0.018 | 0.015 | 0.048 | 0.091 | 0.078 | 0.004 | 0.004 | 0.022 | |
0.80 | Coefficient | 0.003 | −0.013 ** | 0.462 | −0.013 *** | −0.018 | −0.411 ** | 0.447 *** | 0.155 * | 0.002 | −0.002 | 0.005 |
SE | 0.001 | 0.028 | 0.091 | 0.018 | 0.017 | 0.043 | 0.101 | 0.086 | 0.004 | 0.004 | 0.025 | |
0.90 | Coefficient | 0.007 *** | 0.007 | 0.429 *** | 0.020 | −0.043 *** | −0.424 | 0.368 ** | 0.082 *** | −0.003 *** | 0.001 | 0.034 |
SE | 0.002 | 0.035 | 0.109 | 0.022 | 0.022 | 0.035 | 0.124 | 0.106 | 0.005 | 0.005 | 0.03 | |
0.95 | Coefficient | 0.008 * | 0.002 | 0.583 *** | −0.009 | −0.046 * | −0.453 *** | 0.423 *** | 0.09 | 0.000 | −0.005 | 0.04 |
SE | 0.002 | 0.038 | 0.121 | 0.026 | 0.027 | 0.046 | 0.133 | 0.114 | 0.006 | 0.005 | 0.033 |
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Ali, A.R.; Iyiola, K.; Alzubi, A. Harnessing ESG Sustainability, Climate Policy Uncertainty and Information and Communication Technology for Energy Transition. Energies 2025, 18, 5301. https://doi.org/10.3390/en18195301
Ali AR, Iyiola K, Alzubi A. Harnessing ESG Sustainability, Climate Policy Uncertainty and Information and Communication Technology for Energy Transition. Energies. 2025; 18(19):5301. https://doi.org/10.3390/en18195301
Chicago/Turabian StyleAli, Ali Ragab, Kolawole Iyiola, and Ahmad Alzubi. 2025. "Harnessing ESG Sustainability, Climate Policy Uncertainty and Information and Communication Technology for Energy Transition" Energies 18, no. 19: 5301. https://doi.org/10.3390/en18195301
APA StyleAli, A. R., Iyiola, K., & Alzubi, A. (2025). Harnessing ESG Sustainability, Climate Policy Uncertainty and Information and Communication Technology for Energy Transition. Energies, 18(19), 5301. https://doi.org/10.3390/en18195301