The energy-environment nexus is increasingly being affected by the rapid industrialisation and escalating energy demand of ASEAN economies, which has increased environmental degradation. The complex interactions between environmental factors and ecosystem components are explored across various countries, highlighting temporal dynamics that can guide environmental management and conservation efforts. An autoregressive distributed lag (ARDL) model is employed to examine both the long-run equilibrium relationships and the short-run dynamics, offering a more nuanced understanding of the immediate and long-term environmental impacts using panel data from ASEAN+6 countries. The results indicate that there are important long-run relationships between environmental efficiency and important energy and globalisation variables. The higher the consumption of biomass raw materials, the more efficient the environment. On the other hand, the use of fossil fuels has a significant negative impact on environmental efficiency. Renewables play an important role in energy transition strategies, with a higher proportion of renewables in total energy consumption being correlated with higher environmental efficiency. Most importantly, globalisation is strongly correlated with greater environmental efficiency, indicating that global integration and technology transfer mechanisms are conducive to better environmental performance. But short-run dynamics suggest that adjustment is limited, with only lagged globalisation effects being significant, suggesting that short-term deviations from long-run equilibrium last longer than anticipated. The stability of fossil fuel and renewable energy results is verified by robustness tests. Policymakers should focus on investment in renewables, fossil fuel subsidy reform and use international cooperation mechanisms to speed up the energy transition, while addressing short-term adjustment issues through targeted industrial policy measures.

Functional trait plasticity enables invasive plant species to establish and persist across diverse environmental conditions. Following the extensive habitat disturbance caused by the 2010 floods in Khyber Pakhtunkhwa, Verbesina encelioides rapidly spread into disturbed and natural habitats. This study evaluated the phenotypic plasticity and biomass allocation traits of Verbesina encelioides across five contrasting habitats (i.e., cropland, roadside, riverside, urban, and abandoned land). Habitat differences were assessed using analysis of variance with Tukey’s HSD post hoc test, while multivariate analysis was used to examine relationships among functional traits, environmental variables, and habitats. Log-transformed data were used for linear regression analyses to satisfy normality assumptions. Plant functional traits varied significantly among habitats, demonstrating substantial phenotypic plasticity. Riverside vegetation had high functional trait plasticity followed by abandoned habitats. In contrast, riverside populations had fewer branches (14.3 ± 1.4), leaves (184.7 ± 10), lower leaf area (5360 ± 77 cm2), and reduced inflorescence biomass (8.61 ± 2.1 g) than abandoned-land populations. Cropland plants were taller (106 ± 3 cm) with heavier seeds (0.30 ± 0.04 g; p < 0.05), whereas roadside plants had smaller leaves (6.1 ± 0.3 cm) but produced more flowers (38.4 ± 6.2; p < 0.001). Biomass allocation remained relatively stable across habitats (p > 0.05), with greater investment in aboveground than belowground structures. Soil texture, organic matter, temperature, and precipitation were the principal environmental variables associated with trait variation, indicating that local environmental conditions strongly influence plant performance (p < 0.05). PCA revealed that seed traits dominated the first axis and were associated with cropland and riversides. Redundancy analysis indicated that soil properties (sand, silt, pH, EC) and climatic factors (precipitation, temperature) significantly influenced trait distribution. Hierarchical cluster analysis shows that riverside was distinct from other clusters both in functional traits and environmental variables. Inter-trait correlation revealed a significant relationship between leaf and seed traits. Overall, these findings reveal that V. encelioides adjusts its growth and reproductive traits according to habitat conditions. Greater seed weight in croplands may enhance seedling establishment under competitive conditions, whereas increased flower production in disturbed roadside habitats may promote dispersal and reproductive success of the plant. The strong functional trait plasticity of V. encelioides across different habitats highlights its potential to persist under habitat disturbance and changing environmental conditions, therefore posing risk to native plant communities and ecosystem structure. These findings support the need for habitat-based monitoring, protection of native flora and restoration of disturbed areas as part of sustainable land and biodiversity management.

The study uses the Kernel Regularized Quantile Regression (KRQR) method to examine the relationship between Climate Action (SDG13) and Responsible Consumption and Production (SDG12) in high-income countries from 2001Q2 to 2023Q4. The research shows that the impact of SDG13 on responsible consumption is weak at lower quantiles but significantly stronger at higher levels, with the strongest effects observed in the top quantiles. At lower levels of responsible consumption, the effect on climate action is modest but positive, reflecting initial sustainability efforts in high-income economies still reliant on non-sustainable practices. To optimize policy interventions, tailored strategies are proposed across different quantiles. For low quantiles, awareness campaigns, incentives for early sustainable adoption, and support for sectors lagging in sustainability are recommended. For mid-level quantiles, green technology investments, public-private partnerships, and integrated climate policies are recommended. High quantiles require stringent regulations, renewable energy subsidies, and circular economy initiatives.

This study aims to test the long-run and short-run impact of fossil fuel consumption (FC), environmentally friendly technology (EFT) and financial development (FD) in E7 countries within the framework of the Load Capacity Curve (LCC) hypothesis. In line with this objective, this effect is tested using the CS-ARDL procedure for the period 1985-2019. Moreover, the Emirmahmuto & gbreve;lu and K & ouml;se panel causality test is utilised to test the causal link between the variables. CS-ARDL findings show that the LCC hypothesis is not valid and that FC deteriorates environmental quality in E7 countries. Moreover, the findings reveal that FD and EFT improve environmental quality by increasing the load capacity factor (LCF). Causality findings show that there is a bidirectional causal link between LCF and the related independent variables. Based on the empirical findings, a series of policy recommendations are presented. Accordingly, E7 countries should increase the use of renewable energy, transition to a green and circular economy and promote EFT to realise sustainable development (SD).

Introduction The blue economy has become a pivotal framework for achieving sustainable development, emphasizing the responsible utilization of marine and coastal resources while preserving ecosystem integrity. Although biodiversity is central to fisheries, aquaculture, and coastal tourism, its direct role in shaping the blue economy remains underexplored. Furthermore, the interactions of biodiversity with other structural factors, such as financial development, institutional quality, and environmental pressures, are insufficiently addressed in existing studies.Methods This study investigates the determinants of the blue economy by employing panel data from the world's ten highest-income blue economies over the period 2000-2021. To address econometric challenges such as cross-sectional dependence, unit roots, and cointegration, second-generation panel techniques were applied. Long-term relationships were estimated using the Augmented Mean Group (AMG) estimator, and robustness was assessed through complementary econometric tests.Results The empirical findings demonstrate that biodiversity exerts a positive and statistically significant effect on the blue economy. In contrast, financial development is negatively associated with blue economy performance. Institutional quality and per capita income are found to enhance blue economy outcomes, while CO2 emissions exert a detrimental influence. Robustness checks confirm the stability and reliability of these results across alternative specifications.Discussion The results underscore the vital role of biodiversity conservation in fostering sustainable growth within the blue economy framework. However, the negative effect of financial development suggests that existing financial structures do not sufficiently channel resources into environmentally sustainable marine activities. Strengthening institutional frameworks and aligning financial systems with ecological priorities are therefore critical. Moreover, reducing carbon emissions is indispensable to securing long-term resilience of marine ecosystems and ensuring the sustainability of blue economy activities.

This study explores the interplay between industrial robot adoption, green bonds, climate patents, and economic governance in reducing the greenhouse gas emissions intensity of energy consumption (GIEC) in France. Using advanced econometric methodologies, including Kernel Regularized Least Squares and Bayesian Neural Networks, we analyze quarterly data from 2008 to 2019 to uncover both direct and moderating effects. The findings reveal that industrial robots, green bonds, and climate-focused patents significantly reduce GIEC, while household energy consumption has an adverse effect. The moderating role of economic governance in France is pivotal; amplifying emissions from green finance and technology, while curbing those from household energy use, emphasizing the need for targeted, environmentally aligned governance strategies. Sensitivity analyses confirm these patterns across alternative specifications. By revealing the sector-specific channels through which technology, finance, and governance jointly deliver-or derail-decarbonisation, the study sharpens ecological modernisation theory and equips managers and policymakers with an integrated, evidence-based blueprint for aligning industrial efficiency, green finance, and institutional reform to meet national and Paris-Agreement climate goals.

The recent surge in global financial and patent innovations and rising CO2 emissions in the global energy sector have drawn significant attention to China’s transportation industry. This study examines how financial innovations (FINI), patent innovations (PTIN), and bioenergy (BIOE) affect CO2 emissions in China’s transportation sector (TBCO2) using quarterly data from 2000 to 2018. This study employed a novel wavelet local multiple correlation (WLMC) methodology, alongside the time-varying causality test, to examine the time–frequency nexus, addressing a critical gap in the current literature. The WLMC bivariate analyses revealed a negative long-term relationship between PTIN and FINI with TBCO2. At the same time, BIOE showed only a short-term mitigating effect, with PTIN playing a dominant role in this nexus at various frequency levels. Furthermore, the three- and four-variate assessments highlight the consistent positive influence of all included factors on TBCO2. A time-varying causality test also demonstrated significant causal relationships between FINI, PTIN, BIOE, and TBCO2 across different periods, confirming the robustness of our WLMC results. This study provides crucial insights, emphasizing the urgency of promoting FINIs, technological advancement, and bioenergy usage to reduce transportation emissions and pursue sustainable solutions to address China’s environmental challenges.

The term “blue economy” has become synonymous with generating income from maritime pursuits while protecting and improving marine environments. Oceans provide both solutions and boosts to a sustainable environment and economy, given the growing need for resources to accomplish the global food, water, and energy nexus and the rapid reduction in land-based supplies. However, the ecological footprint (EF) is one of the significant factors that may influence the sustained capacity of oceans to deliver economic and environmental value. Therefore, this study aims to investigate the impact of ecological footprints on the sustainability of the blue economy (BE) while controlling for greenhouse gas emissions (GHG), population growth (POT), and economic growth (GDP). The study applied Bayesian neural network (BNN), OLS, fixed effects, and a two-step generalized method of moments on the panel dataset of G20 countries over the period 2000 to 2021. The study shows that the ecological footprint exerts a negative influence on the blue economy, while greenhouse gas emissions, population growth, and economic growth exhibit a positive impact. This study, by realizing the importance of blue economy development in various nations, suggests that all nations must incorporate ocean strategies within their national climate pledges in order to effectively meet the sustainable development goals (SDGs), especially those outlined in SDG 14 (Life Below Water).

The rapid economic growth in the United States has necessitated the exploration of innovative, sustainable approaches to mitigate climate change, particularly in terms of balancing economic development with environmental preservation. This study aims to investigate the asymmetric and long-term effects of artificial intelligence (AI) investment, green electricity adoption, and economic growth on environmental quality in the U.S. Using a novel econometric methodology, the study analyses quarterly time series data from 2012/Q1 to 2021/Q4. The originality of this research lies in its comprehensive assessment of AI investment, green electricity, and economic growth as influential factors on environmental quality, applying advanced techniques such as the ADF unit root test with breakpoints, BDS test, nonlinear ARDL bounds testing, and various diagnostic approaches. The findings reveal several key insights: (i) AI investment exhibits nonlinear and asymmetric effects on environmental quality in the long run, (ii) an increase in AI investment correlates with a higher ecological footprint and diminished environmental quality, (iii) the use of green electricity contributes to a reduction in environmental degradation and fosters sustainable environmental practices, and (iv) economic growth, if not accompanied by eco-friendly practices, can exacerbate ecological deterioration. These results align with both economic and environmental theories, suggesting that green energy solutions play a vital role in promoting sustainability while supporting economic growth. The study emphasizes the need for U.S. policymakers to invest in sustainable initiatives, prioritize research and development of clean technologies, and implement robust eco-friendly policies to effectively combat climate change and achieve long-term climate objectives.

The escalating levels of CO 2 emissions from the transportation sector have been observed to exert deleterious effects on human well‐being and environmental sustainability. To mitigate these emissions, it is imperative to consider the potential contributions of environmental policies and financial innovation. Thus, this study examines the effects of financial innovation and environmental policy (environmental tax–environmental technology) on transport‐related CO 2 emission (TCO 2 E) for BICS (i.e., Brazil, India, China and South Africa), which are characterized by substantial industrialization, rising TCO 2 E and growing environmental concerns. The analysis is complemented by including control variables, notably economic growth and the transition to cleaner energy, to offer a nuanced and holistic perspective on the dynamics governing this critical subject matter. The research adopts a novel econometric approach cross‐sectionally augmented auto‐regressive distributed lags for benchmark estimation and fully modified ordinary least squares–dynamic ordinary least squares for robustness analysis from 2000/Q1‐2018/Q4. The findings revealed that financial innovation and economic growth bolster TCO 2 E and decrease environmental quality in the region. In contrast, environmental tax, environmental innovation and energy transition decrease TCO 2 E. Based on findings, policy suggestions relating to implementing a carbon tax, promoting green financial innovation and others are discussed to decrease TCO 2 E.

The global increase in temperature and climate change signals the need for humanity to reduce greenhouse gas emissions and to adopt eco-friendly lifestyles. The 2023 United Nations Climate Change Conference (COP28) in the UAE emphasized this, urging nations to commit to the Paris Agreement and pursue a greener, carbon -free future. In recent decades, climate change has become a critical issue, primarily because of the extensive use of fossil fuels and conventional energy resources. Economic growth has led to an increase in energy consumption and widespread environmental damage. The present study empirically explores whether any changes in environmental governance, economic complexity, geopolitical risk, and the interaction term influence energy transition and environmental stability in OECD economies over the period 1990-2021. Novel econometric methods, including Westerlund co -integration and the Method of Moments Quantile Regression (MMQR), are employed to address complexities such as cross-sectional dependency and panel causality. The key findings from the MMQR technique showed a positive link between environmental governance and economic complexity in driving sustainable energy transitions, thus bolstering environmental resilience in OECD countries. However, economic complexity counterbalances environmental stability. Significantly, geopolitical risk acts as a moderating variable, enhancing the effects of governance and complexity on sustainable energy practices and environmental stability. Based on these insights, this study recommends strategic initiatives, including investment in ecofriendly technologies, to fast -track the shift to clean energy and strengthen environmental resilience in OECD countries. These strategies align with the broader objectives of global sustainable development, offering a path towards a greener and more sustainable future.

International policymakers have been diligently working towards advancing sustainable development and restoring the green environment, as evidenced by the Paris Agreement and COP27. This study, situated within a comprehensive policy framework, examines the dynamic effects of digital financial inclusion and mineral resources on green economic growth in mineral resources endowment countries from 2000 to 2021. To address slope heterogeneity and cross-sectional dependency, the novel (MMQR) model is applied. We also conduct an asymmetric analysis to detect the moderating and mediating roles of economic governance in the linkage between digital financial inclusion, mineral resources, and green economic growth. Our findings reveal the following: (1) Enhancing digital financial inclusion plays a crucial role in fostering green economic growth, and the synergy between digital financial inclusion and effective economic governance can amplify this positive impact in different quantiles; (2) mineral resources exhibit a sensitive association with GEG, such that excessive mineral resource extraction damages green economic growth. However, the interaction of economic governance with mineral resources transforms the negative influence into a positive one on green economic growth; (3) technological innovation and human development display an asymmetric correlation with green economic growth in different quantiles. The nonparametric panel Granger causality test establishes a significant causal relationship, demonstrating that digital financial inclusion and mineral resources can enhance green economic growth through improved economic governance. This finding holds significant importance for most mineral-rich nations, and we propose policy recommendations to strengthen digital inclusive finance and enhance economic governance.

This research investigates the potential for promoting environmental sustainability in China by analyzing the relationship between financial inclusion, digitalization, and environmental sustainability. Utilizing a multifaceted methodology consisting of quantile-on-quantile regression, Granger causality in quantiles, and wavelet analysis, supplemented by robustness tests, the research reveals a strong positive correlation between financial inclusion, digitalization, and environmental sustainability in China. The findings underscore the pivotal roles of technological innovation and the financial sector in steering the nation towards sustainable development. Essentially, the study indicates that embracing financial inclusion and advanced technology is vital for reinforcing the environmental sustainability of China's economic growth. These insights are crucial for emerging economies aiming to align economic growth with sustainable development objectives through technology innovation and enhanced financial accessibility.

This research delves into the potential for promoting environmental sustainability in China through an in-depth analysis of the interplay between green investment, financial inclusion, digitalization, and their impact on sustainable development. The study adopts a novel three-stage methodology that encompasses quantile-on-quantile regression, Granger causality in quantiles, wavelet analysis, and robustness tests to ensure the reliability of the findings. The results uncover a robust and positive relationship between green investment, financial inclusion, digitalization, and environmental sustainability in China. These findings underline the significance of directing investments towards green resources, fostering technological innovation, and strengthening the financial sector to facilitate the country's transition towards sustainable development. Importantly, the study emphasizes the critical role played by green investment, financial inclusion, and digitalization in enhancing the environmental sustainability of China's current economic growth trajectory. By providing valuable insights, this research becomes a valuable resource for developing countries striving to achieve their sustainable development goals through the strategic utilization of technology innovation, green investment, and financial inclusion.

This study aims to address the gap in the existing literature by investigating the impact of food production and the role of economic governance, such as government effectiveness and regularity quality along with population and economic growth as a control indicator, on food-related CO2 emissions in China. Our analysis employs principal component analysis to assess the food-related CO2 emissions index, comprising food processing, packaging, and household consumption. The study employed an innovative Fourier-based econometric method, using quarterly data from 2000Q1 to 2021Q4. The benchmark estimation from Fourier quantile causality findings reveals that food production, economic growth and population increase food-related CO2 emissions and environmental degradation. In contrast, economic governance is the primary contributor to improving environmental quality by decreasing food-related CO2 emissions in China. Furthermore, the findings also revealed that the effect of food production, economic growth, and population increase on food-related CO2 emissions was overturned when interacting with sound governance. Our findings suggest governance-augmented food production, population, and economic growth to promote environmental and food sustainability in China.

The Paris Agreement and COP27 have been actively working towards a transition to clean energy (SDG-7) and the restoration of the green environment (SDG-13). Therefore, this study was situated within a comprehensive policy framework. This study aims to investigate the effects of environmental governance and economic complexity on energy transition in 20 OECD countries selected for analysis from 1990 to 2021. This study employs the novel MMQR model to account for slope heterogeneity and cross-sectional dependency. Additionally, an asymmetric analysis was conducted to examine the mediating and moderating roles of geopolitical risk in the relationship between environmental governance, economic complexity, and energy transition. The primary findings of this study indicate that (1) environmental governance and economic complexity have a stimulating effect on energy transition at different levels of quantiles. Strict environmental policies have played a critical role in the transition to clean energy. Furthermore, the interaction between environmental governance and geopolitical factors negatively impacts energy transition at various quantiles; (2) economic complexity demonstrates a positive association with energy transition, as countries with high economic complexity possess the necessary resources, capabilities, and resilience to effectively address the challenges and seize the opportunities associated with transitioning to cleaner and more sustainable energy sources. However, the interaction of economic complexity with geopolitics transforms the positive influence of geopolitics into a negative influence on energy transition. The novel nonparametric panel Granger causality test establishes a significant causal relationship, revealing that environmental governance and economic complexity can support energy transition by creating a favorable environment for clean energy adoption, fostering innovation, facilitating effective planning and implementation, enhancing economic resilience, and promoting international collaboration.

Achieving economic progress hinges upon the active and strategic utilization of a nation's inherent resources in economic and financial endeavors. This comprehensive study investigates the intricate dynamics influencing the economic development of the United States, employing a time series dataset spanning from 1991 to 2022. Key factors including total natural resource rents, domestic capital formation, the 'financial risk index (FRI),' and the count of patents filed by both domestic and foreign investors. Rigorous statistical analyses, including the 'Modified Dickey-Fuller' test and 'Bayer-Hanck cointegration' strategy, were employed to extract meaningful insights from the data. Addressing concerns related to endogeneity and serial correlation, advanced techniques such as 'Dynamic Ordinary Least Squares' and 'Fully Modified Ordinary Least Squares' were applied. The findings illuminate the pivotal roles played by natural resource rents and domestic capital formation in propelling sustainable economic development in the United States. Notably, this study sheds light on the positive contributions of both domestic and foreign patent filings to the nation's economic trajectory. Furthermore, enhancements in the FRI are identified as catalysts for fostering sustainable economic growth. In essence, our research contributes to the existing body of knowledge by offering nuanced insights into the multifaceted influences shaping the economic landscape of the United States. The results not only underscore the significance of effective resource management and capital formation but also emphasize the positive impact of innovation, represented by patent filings, and improvements in the FRI on the nation's journey towards sustainable economic growth.

This study explores the opportunities for enhancing environmental sustainability in China through a meticulous examination of the interrelations among green investment, digital financial inclusion, and environmental sustainability. An innovative tripartite methodology encompassing quantile-on-quantile regression, Granger causality in quantiles, and wavelet coherence analysis, complemented by extensive robustness checks is employed to unveil nuanced insights. The findings reveal a robust positive interrelation between green investment, digital financial inclusion, and environmental sustainability, notably within middle to upper quantiles of environmental sustainability, extending across varying levels of digital financial inclusion and environmental sustainability quantiles (0.05-0.95). This synergy is pivotal in middle to high quantiles of both green investment and digital financial inclusion, signifying the critical role of each in environmental conservation. The research accentuates the imperative of strategic investments in eco-friendly innovations and the financial sector to usher China towards a sustainable developmental paradigm. Overall, the findings suggest that green investment, financial inclusion, and play a critical role in enhancing environmental sustainability of China's current economic growth. This study provides valuable insights for developing countries seeking to achieve sustainable development goals through leveraging green investment, and digital financial inclusion. (c) 2023 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

The basic Helix-Loop-Helix (bHLH) superfamily is the most widespread family of transcription factors in eukaryotic organisms, which can activate the expression of genes by interacting with specific promoters in the genes. The bHLH transcription factors direct the development and metabolic process of plants, including flowering initiation and secondary metabolite production, by attaching to specific sites on their promoters. These transcription factors are essential for encouraging plant tolerance or the adjustment to harsh environmental conditions. The involvement of bHLH genes in anthocyanin formation in fleshy fruit-bearing plants, as well as the role of these genes in response to stimuli including drought, salt, and cold stress, are discussed in this article. New concepts and goals for the production of stress-tolerant fruit species are suggested. Furthermore, solid evidence for the critical role of bHLH genes in the growth and development, as well as anthocyanin biosynthesis in fleshy fruit plants, are also presented in this article. This review identifies several future research directions that can shed light on the roles of bHLH genes in fruit-bearing plants and will assist the use of these genes in efforts to breed fruit crop varieties that are more resistant to stress. Generally, there has been little research carried out on the role of bHLHs transcription factor family genes in fleshy fruit-bearing plant species and more in-depth studies are required to fully understand the diverse role of bHLH genes in these species.

Species distribution modelling (SDM) is an important tool to examine the possible change in the population range and/or niche-shift under current environment and predicted climate change. Monotheca buxifolia is an economically and ecologically important tree species inhabiting Pakistan and Afghanistan in dense patches, and species range is contracting rapidly. This study hypothesize that predicted climate change might remarkably influence the existing distribution pattern of M. buxifolia in the study area. A total of 75 occurrence locations were identified comprising M. buxifolia as a dominant tree species. The Maximum Entropy (MaxEnt) algorithm was utilized to perform the SDM under current (the 1970s–2000s) and two future climate change scenarios (shared socioeconomic pathways: SSPs 245 and 585) of two time periods (the 2050s and 2070s). The optimal model settings were assessed, and simulation precision was assessed by examining the partial area under the receiver operating characteristic curve (pAUC-ROC). The results showed that out of 39 considered bio-climatic, topographic, edaphic, and remote sensing variables which were utilized in the preliminary model, 6 variables including precipitation of warmest quarter, topographic diversity, global human modification of terrestrial land, normalized difference vegetation index, isothermality, and elevation (in order) were the most influential drivers, and utilized in all reduced SDMs. A high predictive performance (pAUC-ROC; >0.9) of all the considered SDMs was recorded. A total of about 67,684 km2 of geographical area was predicted as suitable habitat (p > 0.8) for M. buxifolia, and Pakistan is the leading country (with about 54,975 km2 of suitable land area) under the current climate scenario. Overall, the existing distribution of the tree species in the study area might face considerable loss (i.e. rate of change %; −27 to −107) in future, and simultaneously a northward (high elevation) niche shift is predicted for all the considered future climate change scenarios. Hence, development and implementation of a coordinated conservation program is required on priority basis to save the tree species in its native geographic range.