The dampening effect of geopolitical risk and economic policy uncertainty in the linkage between economic complexity and environmental degradation in the G-20

Evaluating both usability and accessibility: the case of access@tour by action - a digital solution for accessible tourism

Aim: Information systems can help improve accessible tourism conditions. During the development of accessible technology, a crucial phase is testing. Notwithstanding, usability testing methodologies often dismiss accessibility. To overcome this gap, this work contributes with an innovative mixed-method testing methodology that integrates accessibility and usability components in technology evaluation. The capabilities of this methodological process are demonstrated through testing the prototype of a mobile solution for accessible tourism - access@tour by action.

Methods: Nine experts evaluated the platform and helped correct accessibility failures. Afterward, 78 end-users tested the prototype. The procedure includes a set of tasks to be performed in the prototype and a final questionnaire. This questionnaire was specifically created for accessibility markets and is itself a significant contribution.

Results: Testing with experts helped improve the prototype's accessibility before it reached end-users. The results with end-users are provided in two distinct ways. First, the task performance measures are presented, which showed overall good completion rates. Second, the results obtained from the questionnaires are disclosed. Overall, the platform was considered relevant, accessible, and easy to use. Still, some minor flaws were identified regarding interface connectivity, symbology, and reading sequence. Thus, improvements are suggested based on obtained feedback.

Conclusion: The testing procedure was essential to gather input from a user point of view, ensuring the final version of the access@tour by action is accessible and, therefore, "usable". This allowed for validating the methodological method, significantly contributing to future research on evaluating technology accessibility.

Revisiting the relationship between geopolitical risk and ecological footprint: A comprehensive analysis based on dual machine learning

Geopolitical conflicts and other risk events are subtly reshaping the global political and economic landscape, gradually disrupting the balance between economic development and ecological sustainability. Understanding the pathways through which geopolitical risks affect the ecological footprint is crucial for achieving ecological sustainability goals. This study employed dual machine learning models for high-precision analysis to deeply explore the intrinsic patterns of how geopolitical risks impact the ecological footprint. Income heterogeneity was also considered. On the one hand, this research constructed a multi-window kernel density estimation (KDE) model to analyze the spatial and temporal characteristics of the ecological footprint. By incorporating dual machine learning models (DML), it innovatively discovered a positive effect of geopolitical risks on the ecological footprint. On the other hand, this study investigated the moderating mechanisms of energy transition through two paths, finding that under the dual-pathway regulation of energy transition, the positive effect of geopolitical risks on the ecological footprint gradually weakens and may even turn negative. Furthermore, compared to high-income countries, geopolitical risks have a stronger impact on the ecological footprint in lower-income countries. The policy recommendations proposed in this study offer a new perspective for decision-makers and are of significant importance for ecological sustainability.

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape

The rapid advancement of artificial intelligence (AI) in the 21st century is driving profound societal changes and playing a crucial role in optimizing energy systems to achieve carbon neutrality. Most G20 nations have developed national AI strategies and are advancing AI applications in energy, manufacturing, and agriculture sectors to meet this goal. However, disparities exist among these nations, creating an "AI divide" that needs to be addressed for regulatory consistency and fair distribution of AI benefits. Here, we look at the linear effects of AI and the Paris Agreement (AI), as well as their potential interaction on carbon neutrality. We also investigate whether geopolitical risk (GPR) can hinder or enhance efforts to attain carbon neutrality through energy transition (ET). To measure carbon neutrality of G20 countries, we employed a robust parametric Malmquist index combined with the fixed-effect panel stochastic frontier model to account for heterogeneity. Results indicate that from 1990 to 2022, carbon neutrality has improved primarily due to technological advancements. Developed G20 countries led in technological progress, while developing countries showed modest gains in carbon efficiency. Using the Driscoll-Kraay robust standard error method, we found that AI has a positive but insignificant linear effect on carbon neutrality. However, the interaction between PA and AI was positive and statistically significant, suggesting that PA augments AI's potential in accelerating carbon neutrality. Energy transition accelerates carbon neutrality in both developed and developing G20 countries. However, the role of energy transition in achieving carbon neutrality becomes negative when the interaction term between energy transition and geopolitical risk (ET × GRP) is incorporated. Regarding control variables, green innovation positively impacts carbon neutrality, whereas financial development has an insignificant effect. Industrial structure and foreign direct investment both negatively affect carbon neutrality, thereby supporting the pollution haven hypothesis. It is recommended that strategies to bridge the "AI divide" and uphold geopolitical stability are crucial to achieve carbon neutrality.

Environmental pollution and officials' promotion: How China's green attention matters

With the growing emphasis on sustainable development, green policies have become a crucial factor influencing both environmental pollution and the career progression of officials in China and other countries. However, the mechanisms behind this relationship remain unclear. This paper aims to enhance the understanding of how environmental pollution impacts official promotion by analyzing the performance of provincial leaders in China and their incentives to address pollution. Using provincial panel data from 1998 to 2020 and a probit model, our study uncovers significant findings. We demonstrate that the intensified green attention by China's central government has notably reduced the promotion prospects for provincial officials with poor environmental protection records, particularly since 2013. Furthermore, our research extends the analysis of micro-level mechanisms, illustrating how the central government's political incentives effectively influence local environmental governance. This study underscores the central government's capability to leverage its personnel system to achieve desired outcomes in sustainable development.

Empirical analysis of trade costs and their impact on carbon emissions in RCEP countries

Global economic integration and environmental issues have attracted widespread attention in recent years. As one of the world's most significant free trade agreements, the Regional Comprehensive Economic Partnership (RCEP) significantly impacts trade and the environment. However, research on the relationship between trade costs and carbon emissions still needs to be completed. This study explores the relationship between trade costs and carbon emissions within the framework of the Trade Benefit Theory, which posits that trade liberalization and openness generate economic benefits through increased efficiency, technological advancement, and economic growth. This study analyzes panel data from 12 RCEP countries from 2001 to 2014, employing static and dynamic panel models to examine the relationship between trade costs and carbon emissions. The analysis utilizes mixed regression, fixed (random) effects models, and the systematic GMM method. The results indicate that decreases in trade costs are associated with reduced environmental pollution, aligning with the Environmental Kuznets Curve (EKC) hypothesis, which posits an N-shaped relationship between trade costs and carbon emissions. Implementing RCEP facilitates a decrease in trade-related pollution, suggesting that reducing trade costs can help mitigate environmental pollution. Furthermore, the observed N-shaped EKC for trade costs and carbon emissions highlights the potential of RCEP to reduce the impact of trade-related pollution.

Sustainable development through green transition in EU countries: New evidence from panel quantile regression

Sustainable development addresses global challenges by promoting practices that balance economic, social, and environmental considerations. Key factors include the shifting to green energy and the integrating of green technology in the sustainable development process. This study investigates the heterogenous effects of green technology development, green energy, R&D expenditures, FDI, economic growth, and urbanization on CO2 emissions in 25 European Union (EU) countries using panel quantile regression over the period 2000-2021. The results, based on panel quantile regression, indicate that green energy decreases CO2 emissions from the 10th to the 90th quantiles, while green technology development increases CO2 emissions at the lower quantiles (10th to 60th) and then turns negative. The robustness of the fixed effect model also confirms the findings of the study. Additionally, panel causality tests indicate no causal link between green technology development and CO2 emissions, but there is bidirectional causality between green energy and CO2 emissions. Therefore, the findings highlight that policymakers should thoroughly evaluate measures and strategies to encourage the development of green technologies and green energy sources to reduce high levels of CO2 emissions. One strategy is to provide financial aid and support technological advances to produce green energy at reduced costs.

The role of renewable energy and total factor productivity in reducing carbon emissions: A case of top-ranked nations in the renewable energy country attractiveness index

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results. 20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates. The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

Evaluating forest depletion and structural change effects on environmental sustainability in Pakistan: Through the lens of the load capacity factor

The pace of species extinction and deforestation has increased dramatically due to the substantial increase in global environmental degradation. This trend is approaching the crucial temperature threshold of 2 °C and calls for more attention. Although previous research has observed the individual impacts of forest depletion, structural change, economic growth, and urbanization on various sustainability outcomes, there has been no previous research into their interrelationships with an emphasis on the load capacity factor (LCF). Furthermore, no previous study has examined the environmental impacts of the abovementioned variables by contrasting the results of LCF and CO2 emissions in Pakistan. Therefore, this research suggests a theoretical framework that integrates these concepts, provides a roadmap for an effective and sustainable mitigation strategy for Pakistan and compares LCF results with CO2 emissions. Using the time-series data from 1970 to 2021, a unique and sophisticated dynamic Autoregressive Distributed Lag (DARDL) technique, the authors found that (i) a 1 % rise in forest depletion leads to a decline in load capacity factor by 0.026 %. (ii) A one per cent upsurge in structural change fosters environmental sustainability by raising the load capacity factor by 0.084 %. (iii) An increase of 1 % in economic growth dwindles the load capacity factor by 0.027 %. (iv) A one per cent surge in urbanization enhances the load capacity factor by 0.029 %. The findings suggest that Pakistan's Government should promote afforestation by emphasizing the constructive role of structural change in achieving environmental sustainability.