Whether geopolitical risk drives or impedes the clean energy transition is empirically contested. We examine the effect of country-specific geopolitical risk (GPR) on green hydrogen innovation using a balanced panel of 33 countries from 2000 to 2024, a multi-dimensional patent value outcome, and an instrumental variable strategy based on terrorism shocks. The results show that: (1) country-specific GPR is associated with substantially higher green hydrogen patent value, with the effect strongest on the market dimension of patent value; the response extends across all four hydrogen production pathways, but conditioning on carbon-market adoption reveals that without carbon pricing no pathway responds, while under carbon pricing the response concentrates almost exclusively in green hydrogen; (2) the effect varies across development status, energy-import dependence, and international cooperation, concentrating in Global South economies and absent among Advanced North economies that sit at a capacity ceiling; (3) threshold regressions on four conditioning variables reveal distinct non-linear moderating structures: governance amplifies the response monotonically, the policy ecosystem exhibits a critical-mass property with amplification concentrated above the upper threshold, human capital shows a pattern consistent with an interruption at intermediate endowments, and the response declines across the energy poverty distribution, turning negative among the six economies in our sample where deprivation is most severe. We report the human-capital and energy-poverty patterns as conditional regularities; the mechanisms behind them are not directly tested. These results provide an empirical foundation for designing hydrogen policies that internalize governance capacity, policy density, human-capital configuration, and the security of national energy provision.

This paper examines how oil price shocks shape hydrogen patent innovation across 33 countries over the period 2000-2024, using the panel local projection framework with an asymmetric shock decomposition. Four findings emerge. First, the aggregate response of hydrogen patenting to symmetric oil price shocks is statistically null, a result that conceals rather than reflects the underlying relationship. Once shocks are decomposed into cumulative positive and negative components, a clear asymmetry emerges: positive oil shocks raise hydrogen patent value by approximately 2 percent over a five-year horizon, while negative shocks suppress it by approximately 3 percent, with the downside effect at least as large as the upside. Second, the asymmetry is concentrated in fossil-based hydrogen through an incumbent-leverage mechanism, while green hydrogen via water electrolysis responds significantly to positive shocks at a comparable magnitude but remains insulated from price declines; by-product and biomass routes show weak or negligible responses. Third, the response is conditioned by country-level infrastructure and institutions: hydrogen R&D capacity, CCUS deployment, and institutional quality amplify the positive-shock response, with no comparable amplification of the negative-shock contraction. Fourth, the positive-shock stimulus operates only in countries with a supporting climate-policy framework, whether hydrogen-specific coordination or economy-wide carbon pricing, while countries without such frameworks show no response; the negative-shock suppression appears in the full sample but not cleanly within subgroups. These findings qualify the intuition that high fossil prices automatically catalyze the green transition and indicate that both framework adoption and countercyclical R&D support are needed to make hydrogen innovation robust to the oil price cycle.

Economies around the world are currently facing the significant challenge of balancing economic growth with ecological sustainability. While this is a global issue, emerging economies, because of their substantial environmental, demographic, and economic influence, deserve urgent attention. In light of the above, this study examines the factors shaping environmental sustainability (proxied by consumption-based carbon emissions) in emerging seven economies (E7) from 2004 to 2021. The study deployed the novel Method of Moment Quantile-based Regression (MMQR) and revealed that natural resources have negative effects on ecological sustainability by increasing consumption-based carbon emissions (CCO2) in E7 economies across all quantiles. Conversely, financial inclusion, financial globalization and the adoption of renewable energy decrease CCO2 thereby improving performance. In other words, the study established that higher dependence on resource endowment intensifies ecological harm whereas financial inclusion, renewable energy deployment and financial globalization strengthens ecological quality. Given these discoveries, the study underlined the criticality of enhancing resource governance to effectively guarantee that resources are exploited responsibly, catalyzing the uptake of green energy to delink growth from resource-based emissions and encouraging financial institutions to offer sustainable financial instruments. These strategies can bolster environmental well-being while also promoting inclusive economic development.

The allocation of public budgets to research and development plays a vital role in advancing climate welfare and facilitating the energy transition. Energy transition focuses on sustainable development goal −7 (SDG-7). By discussing the importance of energy transition and research and development, the current study elaborates on the pathway to resilient energy. This study investigates the influence of public research and development budgets on energy transition in 20 leading sophisticated economies over the period from 1995 to 2022. Using the method of moment quantile regression (MM-QR), the findings reveal a positive association between public renewable energy R&D budgets and the Energy Transition Index (ETI) across all quantile distributions. The effect gets weakened slightly at higher quantiles. While energy efficiency RD&D budgets demonstrate a positive association, the results are statistically insignificant. Public spending on storage/other technologies and high-tech industry demonstrates a negative impact in the lowest quantile, transforming to a positive and significant effect in higher percentiles. The influence of control variables is further explored. Institutional quality and technological innovation exert a positive and significant effect on energy transition across all quantiles, while economic complexity demonstrates a negative impact, particularly pronounced in lower development stages. The study suggests that governments within these leading economies should prioritize public R&D budgets, particularly for low-cost renewable energy solutions across domestic, industrial, and transportation sectors. Furthermore, policies suggestions is towards carbon-free electrification, electric vehicle adoption, and hydropower generation can accelerate progress.

This research examines how the adoption of industrial robots (IR) has influenced the energy intensity of Chinese manufacturing firms between 2011 and 2019. Using a combination of fixed-effects (FE-OLS), instrumental-variable (IV/2SLS), and Method of Moments Quantile Regression (MMQR) estimations, the analysis investigates both average and distributional effects. In addition, an Enterprise Innovation and Efficiency Index (EIEI) is introduced as an exploratory indicator that summarizes firms’ investment capacity, financial structure, and managerial incentives. The findings reveal that industrial robots are generally associated with lower energy intensity, with stronger reductions observed among firms at the upper end of the distribution. The business cycle itself shows no direct influence, although its interaction with IR becomes relevant during periods of economic expansion. In highly concentrated industries, energy intensity tends to fall, but this effect weakens when automation deepens. Environmental regulation on its own is not significant; however, when combined with IR, it can temporarily raise energy use as firms adjust to new technologies. The EIEI results indicate that companies with stronger internal capabilities—greater investment resources, sound financial positions, and effective management—are better placed to achieve lasting energy efficiency. These findings emphasize the importance of promoting robotic automation in energy-intensive firms while aligning complementary strategies for sustainability.

Economic policy uncertainty (EPU) and oil price shocks can have a substantial influence on economic development, nevertheless their asymmetrical impacts on green economic growth are not widely investigated. Therefore, this study examines the asymmetric effect oil price and EPU on green economic growth using advanced nonlinear techniques, and quarterly data from 1990Q1 to 2020Q4. The empirical findings show that higher oil price induce a decline in green economic growth, while a decrease in oil price promotes green economic growth. On the other hand, the study found that an increase in EPU has positive and significant effect on green economic growth while a decline in EPU has an adverse and substantial impact on sustainable economic growth. These outcomes highlight the need for decisionmakers in South Africa to effectively leverage rising policy uncertainty to promote green growth while decreasing oil reliance to mitigate the negative influence of higher oil prices and capitalize on the benefits of falling prices.

This study aims to determine environmental quality trends by exploring forestry coverage trends in Latin America and the Caribbean through the lens of club convergence. Its relevance lies in addressing the region's heterogeneous sustainability challenges amid global net zero commitments. Employing club convergence methodology and regression techniques such as Feasible Generalized Least Squares (FGLS) and Panel-Corrected Standard Errors (PCSE), the study evaluates the influence of socioeconomic factors—particularly productivity—on forestation dynamics. Results reveal distinct trajectories: Club 1 countries (e.g., Brazil, Costa Rica) show increasing forest cover driven by productivity gains and manufacturing expansion; Club 2 countries (e.g., Mexico, Chile) exhibit an inverted U-shaped pattern with recent declines; and Club 3 countries (e.g., Nicaragua, El Salvador) face sharp forest loss influenced by population growth and material consumption. A key contribution is the hypothesis of productivity efficiency, proposing that higher productivity reduces demand for natural inputs, thereby lessening forest exploitation while supporting industrial growth. This insight aims to advance understanding of sustainable development by linking economic efficiency with environmental outcomes. Practically, findings highlight the need for differentiated policy approaches recognizing regional heterogeneity and the central role of productivity improvements in fostering forest conservation. The study thus informs targeted strategies for balancing economic growth and ecological preservation in Latin America and the Caribbean's net zero transition.

The mobilization of private capital under ESG mandates relies on the untested premise that ESG performance, scored at the firm level and aggregated to countries, converts into the public outcomes measured by the Sustainable Development Goals. Whether that conversion occurs, and under what conditions, remains contested. This study investigates the relationship between country-level ESG performance and SDG attainment across 61 countries over 2000-2024. We construct a novel entropy-weighted composite ESG index integrating 60 indicators across environmental, social, and governance dimensions. ESG performance raises SDG attainment on average, and this effect survives instrumental-variable, sensitivity, and placebo tests. Threshold regression then uncovers nonlinear dynamics: using AI, renewable energy, and institutional quality as threshold variables, the ESG–SDG relationship is negligible below critical thresholds but amplifies once countries surpass them, a pattern consistent with capacity-dependent regime switching. ESG adoption thus advances sustainable development only where complementary information, infrastructure, and institutional capacity are already in place.

Industrial automation is reshaping the structural energy profile of advanced economies, yet its distributional consequences for Energy Security Risk (ESR) remain poorly understood. This paper estimates the asymmetric effects of industrial robot adoption on the ESR index across ten leading economies using annual data over 2000–2019 and the multivariate quantile-on-quantile regression (m-QQR) framework. Our estimates reveal a three-phase dynamic in six economies (China, Denmark, France, Germany, Italy, and the United States): at low automation intensity, robot density elevates ESR because early-stage deployment imposes novel electricity loads on existing grid infrastructure before efficiency gains materialize, generating transitional deterioration in energy reliability; the relationship reverses at intermediate levels as efficiency gains and renewable grid integration dominate; and high automation intensity yields sustained ESR improvements. Sweden stands apart, exhibiting uniform ESR reductions across the entire automation distribution, attributable to the complementarity between its mature renewable energy base and automated manufacturing. In contrast, Japan, South Korea, and Singapore display a sign reversal at the automation frontier, where rising robot density deteriorates energy security — consistent with a tipping-point dynamic in which automation-intensive electricity demand outpaces efficiency gains in structurally import-dependent economies. These findings demonstrate that the automation–ESR relationship is non-monotonic, asymmetric, and context-contingent. Effective energy security policy must be calibrated to each economy's stage in the automation transition and its structural position in global energy trade.

Developed economies face mounting environmental challenges from excessive resource consumption, but we lack clear evidence on how environmental policies can best address these issues. This study investigates how environmental governance shapes resource use and ecological efficiency across nine OECD countries from 1997 to 2020. Our analysis reveals that stronger environmental policies significantly improve eco-efficiency: a 1 % increase in environmental governance effectiveness enhances eco-efficiency by 0.65-0.95 %, with the strongest effects observed in countries currently showing lower ecological efficiency. We find that increasing energy transition efforts and research and development investment each contribute to improved eco-efficiency (0.07-0.11 % and 0.19-0.35 % respectively), while excessive resource use reduces it by 0.07-0.03 %. Notably, our study introduces a novel analytical approach by examining how environmental policies moderate the negative impacts of resource overuse across different levels of ecological efficiency. This relationship proves especially important for countries struggling with lower eco-efficiency, where strong environmental governance can effectively offset the harmful effects of excessive resource consumption. These findings remain consistent across multiple measures of eco-efficiency and trade indicators, offering robust evidence for policymakers. Our research provides practical guidance for balancing economic development with environmental protection through targeted policy interventions, particularly in resource-intensive economies working to improve their ecological performance.

Algeria’s resource-dependent economy faces significant challenges in balancing hydrocarbon reliance with environmental sustainability, yet existing research largely overlooks the comprehensive load capacity factor (LCF) metric in favor of traditional emissions analyses. This study examines the relationships between the LCF and key economic–environmental factors in Algeria from 1980 to 2023, including total natural resource rents, energy transition, technological innovation, GDP, primary energy consumption, and urbanization. Using ARDL and DARDL econometric approaches complemented by a kernel-based regularized least squares analysis, the research captures both linear and nonlinear relationships while accounting for asymmetric dynamics in short- and long-term perspectives. The findings reveal that natural resource rents, technological innovation, and urbanization significantly impair Algeria’s LCF, while primary energy consumption shows a minimal positive impact. The energy transition initiatives demonstrate mixed effects, highlighting the complexities of green energy implementation in resource-dependent economies. These results suggest that Algeria’s sustainable development requires targeted policies focusing on resource management efficiency, environmentally conscious urban planning, and green technology adoption, providing valuable insights for other resource-rich nations pursuing similar sustainability transitions.

Amid escalating environmental crises—ranging from biodiversity loss to climate instability—the circular economy has emerged as a promising pathway to align economic growth with ecological limits. The objective of this study is to examine the asymmetric impact of a novel composite circular economy index (CEI)—constructed via entropy weighting—on the load capacity factor (LCF), a holistic sustainability metric, across 27 EU member states over 2010–2023. Employing the method of moments quantile regression (MMQR) and controlling for GDP, foreign direct investment, trade openness, employment, and population growth, the main findings indicate pronounced heterogeneity: positive CEI shocks yield a 1.219 percent increase in LCF at the 90th quantile versus just 0.229 percent at the 10th, revealing a “sustainability premium” for high-performing economies, while negative shocks inflict a −5.253 percent decline at the 90th quantile, exposing their greater vulnerability. Low-LCF countries, by contrast, display relative resilience to downturns, likely due to less entrenched circular systems. Panel Granger causality tests further reveal bidirectional feedback loops between LCF and economic growth, investment, and labor markets, alongside a unidirectional effect from trade openness to enhanced sustainability. These insights carry clear policy implications: high-LCF nations require safeguards against circularity backsliding, whereas low-LCF members need capacity-building to convert latent resilience into sustained gains—together forming a nuanced blueprint for achieving the EU’s 2050 climate-neutrality ambitions.

Artificial intelligence is increasingly recognized for its potential to enhance ecological quality by streamlining production processes, reducing environmental emissions, and improving ecological monitoring systems. However, the influence of artificial intelligence on ecological quality is neither uniform across different stages of technological adoption nor consistent across national contexts. The central objective of this study is to investigate the asymmetric and stage-specific effects of artificial intelligence adoption on ecological quality within the Group of Seven (G7) economies over the period from January 2000 to December 2019. Employing a novel multivariate quantile-on-quantile regression framework, this research examines how varying intensities of artificial intelligence adoption impact different levels of ecological outcomes. The results indicate that artificial intelligence exerts a modest positive effect on ecological quality during early stages of adoption, a more substantial effect during transitional phases, and a significantly positive influence at advanced stages of integration. To address endogeneity concerns-particularly reverse causality and omitted variable bias-this study utilizes an instrumental variable multivariate quantile regression approach, using lagged values of artificial intelligence adoption as an instrument. The findings are validated through robustness checks using kernel regularized least squares and standard quantile regression techniques. The results also reveal considerable variation across countries, highlighting the necessity for country-specific and stage-aware policy interventions. Accordingly, the study offers detailed, actionable recommendations tailored to the adoption stage of each G7 member to maximize the ecological benefits of artificial intelligence. This research provides a rigorous, causally grounded analysis of how artificial intelligence can be harnessed to advance environmental sustainability in highly industrialized economies.

This paper tests the 'Dutch disease' mechanism with a shift-share design in 98 developing countries during 1992-2012, a period in which China's soaring demand for primary commodities generated resource windfalls for natural resource-exporting countries. At the aggregate level, we find that resource windfalls increased the growth rate of the manufacturing sector but decreased the growth rate of the agricultural sector and public sector. This positive effect of resource windfalls on manufacturing growth is most likely attributable to the expansion of downstream industries of the resource sector through forward linkages. In disaggregated analyses by industry, we find that the output of the textiles industry was negatively affected by resource windfalls, which could result from exchange rate appreciation due to commodity boom. The average wage increased across a range of other industries during resource boom.

This study examines the relationship between national research, development, and demonstration (RD&D) budgets - both in total and split into clean and fossil categories - and environmental quality, as measured by the Load Capacity Factor (LCF). The analysis covers eight advanced economies from January 1990 to December 2023 and applies a kernel-based quantile method designed to capture non-linear and heterogeneous effects. The results indicate that the link between energy budgets and environmental outcomes is not uniform across countries or quantiles. Moreover, aggregate and clean energy budgets show consistent positive impacts in Germany, the United States, and Sweden, particularly at higher levels of technological maturity and environmental performance, which supports the presence of threshold effects. On the other hand, France and Norway exhibit weak or negative associations, which are likely explained by energy system saturation or misaligned RD&D strategies. Meanwhile, dirty energy budgets produce limited benefits, with some short-term improvements at low environmental performance levels in Canada and Australia. Therefore, clean energy budgets are more likely to generate reliable gains, especially in countries with strong innovation capacity and supporting infrastructure. However, mixed results are found in Japan, France, and Sweden. Based on these findings, RD&D policies should be context-specific and aligned with the maturity of energy systems, the level of innovation, and prevailing environmental conditions. Instead of uniformly increasing RD&D budgets, policymakers in leading investor countries should focus on targeted allocations to clean energy, supported by enabling infrastructure and appropriate regulatory frameworks, in order to maximize environmental gains.

Energy transition (ET) is considered a key strategy to combat climate change and environmental degradation, making it a critical imperative for all countries. A transition to clean energy is essential for achieving decarbonization goals. Considering the significant role of the digital economy (DE), this study explores the relationship between ET and renewable energy (RE) innovation in Belt and Road Initiative (BRI) countries from 2002 to 2019. The study focuses on four categories of International Patent Classification (IPC) related to solar, wind, biomass, and geothermal energy technologies. Panel quantile-based analysis is employed to assess the impact of ET in the presence of DE on RE innovation. The main findings indicate that (i) innovation in all categories of energy technologies studied plays a pivotal role in assessing ET in BRI economies, (ii) the DE substantially contributes to enhancing ET, and (iii) BRI countries should prioritize increasing innovation in RE, (iv) the study discusses various policy implications tailored for BRI countries accordingly.

This study investigates the impact of environmental and economic factors on carbon emissions (CAR) in China from 2007 to 2020, considering the Environmental Kuznets Curve (EKC) hypothesis. We employ the MM-QR approach to analyze asymmetric relationships between Green Total Factor Productivity (GTFP), Fiscal Expenditure Technology (FTE), Green Technology Innovation (GTI), Innovation Level (INN), Gross Domestic Product (GDP), Human Capital (HC), and carbon emissions (CAR). Findings reveal that GTFP, FTE, GTI, and INN exert significant negative impacts on cCAR, with these effects strengthening at higher quantiles. This suggests that advancements in green technology and fiscal policies promoting environmental technologies contribute to reduced carbon emissions. Conversely, GDP exhibits a positive association with CAR, potentially reflecting the initial stages of the EKC. GDP2 is also positively associated, indicating a potential turning point towards environmental degradation at higher income levels. Based on these results, the study proposes policy recommendations to enhance ecological well-being and achieve net-zero emissions. These include fostering human capital development for skilled labor in green sectors, promoting the development and adoption of green technologies, and increasing fiscal expenditure on environmental research and development (R&D). This research contributes to the field of environmental management by providing empirical evidence on the effectiveness of green economic policies and technological advancements in reducing carbon emissions. The findings offer valuable insights for policymakers aiming to achieve sustainable development in China and similar economies.

The accelerating degradation of the global environment, primarily driven by dependence on fossil fuels, has intensified the urgency for energy transitions toward renewable sources. While the literature on energy transitions is expanding, the role of environmental governance, particularly the stringency of environmental policies, remains insufficiently understood. This study addresses this gap by empirically examining how environmental policy stringency influences national energy transitions. Using a balanced panel of 29 countries over the period 2010–2024, we construct an energy transition indicator and estimate its relationship with policy stringency while controlling for macroeconomic and structural factors such as income, trade openness, and foreign direct investment. To mitigate endogeneity and cross-sectional dependence, we employ robust econometric techniques, including Instrumental Variables (IV) two-step Generalized Method of Moments (GMM) and IV two-stage least squares estimators. The results provide strong evidence that stricter environmental policies significantly accelerate the shift toward cleaner energy sources. Furthermore, the findings highlight the complementary roles of financial innovation in mobilizing green investments and economic complexity in facilitating sustainable energy adoption. These insights underscore the critical importance of stringent environmental governance in achieving global decarbonization goals and inform policymakers on the design of effective regulatory frameworks to foster energy transitions.

The MENA region faces a critical challenge: balancing economic growth spurred by foreign direct investment (FDI) with environmental sustainability. While FDI can bring technological advancements and capital, concerns exist about its potential to exacerbate environmental degradation, particularly carbon emissions. This study addresses this knowledge gap by investigating the environmental consequences of disaggregated FDI inflows (resource extraction, manufacturing, and services) on emissions from various economic sectors in the MENA region from 1990 to 2022 by employing the system generalized method of moments to test the "pollution haven" and "pollution halo" hypotheses at both aggregate and sectoral levels. Our findings reveal that, at the aggregate level, total FDI reduces economy-wide and tertiary sector emissions, primarily due to services FDI promoting clean technologies and structural transformation, supporting the "pollution halo" hypothesis. However, a disaggregated analysis shows that resource extraction and manufacturing FDI increase emissions across all sectors by expanding pollutive production and outsourced activities, aligning with the "pollution haven" effect. Conversely, services FDI consistently decreases emissions across all sectors, enhancing environmental quality and conforming to the "pollution halo" hypothesis. These insights hold policy significance for targeted incentives: promote clean services FDI while regulating resource extraction and manufacturing flows to balance economic growth and environmental sustainability. By investigating FDI's disaggregated effects, this study refines our understanding of environmental impacts and informs tailored policy strategies in the MENA region.