Q43 - Energy and the MacroeconomyNávrat zpět

Faced with domestic and international responsibilities, China urgently needs to coordinate various regions to achieve carbon peak in an orderly manner. As core regions driving economic expansion and primary hubs of energy consumption, the Beijing-Tianjin-Hebei (BTH) and the Yangtze River Delta (YRD) regions are substantial contributors to carbon emissions in China. To address regional disparities in carbon emission management, this study estimates CO₂ emissions from 1990 to 2021, employing the STIRPAT model to analyse influencing factors. The findings reveal that the key factor affecting CO₂ emissions in both regions is population. Energy intensity plays a larger role in the BTH, while urbanization affects the YRD more significantly and industrial structure notably affects emissions only in the YRD. Additionally, the PSO-SVR model is integrated with dynamic scenario analysis to predict carbon peaking under various scenarios. Predictions suggest peaks in 2026 for the BTH and 2028 for the YRD under a baseline scenario, with earlier peaks under low-carbon scenarios and delayed peaks under high-carbon scenarios. The research offers insights for region-specific carbon reduction management strategies, addressing regional disparities and guiding effective mitigation efforts.

Amidst ongoing global worries over climate change and its ecological ramifications amidst rapid economic growth, the United Nations has set forth a comprehensive agenda known as the Sustainable Development Goals (SDGs), aiming to achieve them by 2030. These goals are tailored to foster sustainable socio-economic progress while enhancing the quality of the global environment. Therefore, this study explores the connections between load capacity factor, green quality of energy mix, financial development, economic growth and natural resources in alignment with SDGs 7, 11, 13 and 12, focusing on China from 1981 to 2021. The study introduces the rolling window kernel-based regularized least squares (RWKRLS) method to assess the interrelationship. The RWKRLS analysis exposes that a green quality of energy mix and financial development positively influence ecological quality. Furthermore, natural resource consumption and economic growth affect ecological quality negatively. Drawing on these critical time-varying interrelationships, a set of interactive policies aligned with the SDGs is proposed, specifically tailored to address China's unique context.

We shed light on the German-Russian relationship, focusing on the complexities that underlie their economic policy uncertainties over the period 1994–2023. We start with static unconditional correlation and dynamic conditional correlation analyses to examine the strength and temporal variations in the correlation between economic policy uncertainties of these two major countries. We then utilize standard and time-varying Granger causality analysis to uncover causal relationships. Importantly, we use a novel energy policy uncertainty index for Germany to explore interconnections between economic and energy policy uncertainties. We emphasize the importance of incorporating energy policy uncertainty in economic policy decision-making and international cooperation.

In today's rapidly evolving world, the transition towards green energy remains momentous in attaining ecological sustainability. In this respect, the present study intends to elucidate factors influencing the green energy transition in OECD economies from 2004 to 2020. We use several diagnostic measures to validate the heterogeneity of slopes and cross-sectional dependence in the panel. Nevertheless, cointegration exists between the study variables, such as green energy, political risk, economic risk, financial risk, human capital, eco-innovation and energy efficiency. Using fixed effect and random effect approaches, we conclude that political risk, human capital and energy efficiency are significant and leading drivers of green energy transition in the region. However, economic expansion, financial risk and economic risk are significant barriers to transitioning towards green energy in the selected economies. The outcomes are robust, as authenticated by linear regression with a heteroskedastic panel-corrected standard error approach. We recommend the minimization of political, financial and economic risk, while improvements in environment-related R&D investment could further boost the transition process towards green energy.

This study investigates the relationship between economic complexity, globalization, energy consumption patterns and CO₂ emissions in 12 energy-importing emerging economies from 1996 to 2020. Employing panel data analysis, the autoregressive distributed lag (ARDL) model is utilized. The findings reveal a U-shaped relationship between economic complexity and air pollution, supporting the environmental Kuznets curve (EKC) theory. Renewable energy demonstrates a significant ability to reduce CO₂ emissions over the long term, while fossil fuel use exacerbates environmental degradation. Economic globalization is associated with increased CO₂ emissions, contradicting expectations. The short-term results align with the long-term findings, highlighting significant country-specific variations. The policy implications highlight the necessity of promoting renewable energy adoption and reducing reliance on fossil fuels. This research contributes to EKC literature by focusing on energy-importing economies, emphasizing the importance of multidimensional analyses in environmental policy formulation. The study underscores the critical role of renewable energy investment and carbon pricing strategies in mitigating environmental degradation while encouraging sustainable development pathways.

In the pursuit of sustainable development, economies such as China are placing a paramount emphasis on significantly augmenting the utilization of renewable energy sources. This marks a departure from conventional research approaches that solely focused on macroeconomic determinants while investigating patterns of renewable energy consumption. Thus, this research pursues to witness the relationship between the political risk index (PRI), renewable electricity output (RELOP), public-private partnership investment in energy (PPINENR), and renewable energy consumption (RECNS) in China from 1984 to 2022. For data estimations, this study utilizes time series methods, which include DF-GLS and Johansen cointegration for unit root and long-run equilibrium with FMOLS, DOLS and CCR as primary methods. The research also employs the least squares method with break years and robust least squares as robustness check methods while for causal relationships, we deploy the Granger causality approach. The outcomes assert that variables are found stationary at differences and long-run equilibrium is confirmed among variables. The empirical estimations predict that GDP and PPINENR reduce the RECNS in China in both short and long term. Furthermore, PRI and RELOP enhance renewable energy consumption in the short as well as long run. Therefore, policymakers should mostly focus on the encouraging role of PPINENR towards renewable electricity to enhance RECNS in developing economies, particularly in China. To achieve the targets of COP27, China should increase its focus on the efficient utilization of public-private partnership investments and also manage the political risks in the economy to promote renewable energy consumption and achieve a sustainable environment. Moreover, the causality analysis unearths that PRI could be utilized along with other variables to enhance RECNS in China. The robustness check asserts similar and robust outcomes.

The paper focuses on the oil price-macroeconomy relationship by means of analyzing the impact of oil price changes on economic growth in the United States of America from 1947: Q1 to 2001: Q4. First, we present the most important oil shocks that occurred in the second half of the last century. Then we describe three different proxies of oil price changes. Second, we provide large analyze of the impact of oil price increases and decreases on the GDP using the different types of measurements of oil shock. The results suggest that an oil price increase, which overcomes it's own maximum values from three previous years, has greater negative effect on production growth than if it simply corrects the previous decreases or exceeds a one year maximum only. The paper also presents a hypothesis that oil price decreases do not contribute to economic growth. All these results allow us to maintain the nonlinear interpretation of the analyzed relationship suggested by Hamilton.