Search Results (111)

Tourism is recognized as one of the key enabling industries driving Thailand’s strategic transition toward the Thailand 4.0 economic model. This paper presents the development of a national technology roadmap to support the transition toward sustainable tourism in Thailand, conducted between January and October 2024. The primary objective is to promote tourism growth that is environmentally responsible, economically viable, and socially inclusive. The roadmap was developed through a combination of a literature review, statistical data, and group discussions with tourism professionals. The roadmapping process focused on aligning external drivers, strategic goals, and the current capabilities of Thailand’s technological ecosystem. The key drivers identified include environmental concerns, the advancement of digital platforms, the growing trends of an aging population, diversity, and inclusion, political instability, and the emergence of middle-income nations. The resulting roadmap outlines a strategic vision for Thailand’s tourism sector from 2024 to beyond 2030. It emphasizes the transition linking sustainability and eco-design principles to smart tourism, metaverse applications, and personalized travel experiences. Priority areas include the adoption of green technologies, sustainable practices, and advanced digital platforms. This study further recommends research and development (R&D) initiatives in sustainability, biodiversity conservation, Data Analytics, Cybersecurity, and E-Tourism solutions. Ultimately, this roadmap provides actionable guidance for tourism stakeholders in defining their roles, responsibilities, and contributions toward achieving a sustainable tourism future in Thailand. Full article

In this study, the relationship between economic growth and carbon emissions for the period 1968–2022 in Turkey was evaluated within the framework of the EKC (Environmental Kuznets Curve) hypothesis. In addition, the impacts of renewable energy consumption and agricultural value added on carbon emissions were analyzed using the ARDL bounds testing approach. The validity of the results was also tested using the FMOLS and DOLS methods. The findings confirmed the existence of a cointegration relationship between carbon emissions and per capita income, renewable energy consumption, and agricultural value added. Long-term analyses indicate that renewable energy consumption reduces carbon emissions, whereas growth in agricultural value added leads to an increase in emissions. In addition, it has been determined that the EKC hypothesis is valid in both the long and short terms and that increases in per capita income raise emissions up to a certain threshold and have a mitigating effect when this threshold is exceeded. The results of the short-term analysis showed that the effects of renewable energy consumption vary across periods, and that agricultural value added increases emissions in the short term. This study provides empirical evidence for Turkey by incorporating sectoral variables within the EKC framework and offers meaningful insights for policymakers regarding the environmental impacts of agricultural value added and renewable energy use in the context of a developing country. Accordingly, fiscal policy instruments such as green taxation, carbon credit trading mechanisms, and financial and agricultural subsidies should be more effectively utilized in Turkey to support structural transformation in agriculture and promote the use of clean energy, in line with the findings that suggest the need for targeted agricultural and energy policies aligned with Turkey’s SDG commitments. Full article

In the face of increasingly complex urban challenges, a critical question arises: can urban ecosystems maintain resilience, vitality, and sustainability when confronted with external threats and pressures? Taking Kunming—a plateau-mountainous city in China—as a case study, this research constructs an urban ecosystem resilience (UER) assessment model based on the DPSIR (Driving forces, Pressures, States, Impacts, and Responses) framework. A total of 25 indicators were selected via questionnaire surveys, covering five dimensions: driving forces such as natural population growth, annual GDP growth, urbanization level, urban population density, and resident consumption price growth; pressures including per capita farmland, per capita urban construction land, land reclamation and cultivation rate, proportion of natural disaster-stricken areas, and unit GDP energy consumption; states measured by Evenness Index (EI), Shannon Diversity Index (SHDI), Aggregation Index (AI), Interspersion and Juxtaposition Index (IJI), Landscape Shape Index (LSI), and Normalized Vegetation Index (NDVI); impacts involving per capita GDP, economic density, per capita disposable income growth, per capita green space area, and per capita water resources; and responses including proportion of natural reserve areas, proportion of environmental protection investment to GDP, overall utilization of industrial solid waste, and afforestation area. Based on remote sensing and other data, indicator values were calculated for 2006, 2011, and 2016. The entire-array polygon indicator method was used to visualize indicator interactions and derive composite resilience index values, all of which remained below 0.25—indicating a persistent low-resilience state, marked by sustained economic growth, frequent natural disasters, and declining ecological self-recovery capacity. Forecasting results suggest that, under current development trajectories, Kunming’s UER will remain low over the next decade. This study is the first to integrate the DPSIR framework, entire-array polygon indicator method, and Grey System Forecasting Model into the evaluation and prediction of urban ecosystem resilience in plateau-mountainous cities. The findings highlight the ecosystem’s inherent capacities for self-organization, adaptation, learning, and innovation and reveal its nested, multi-scalar resilience structure. The DPSIR-based framework not only reflects the complex human–nature interactions in urban systems but also identifies key drivers and enables the prediction of future resilience patterns—providing valuable insights for sustainable urban development. Full article

Saffron is a high-cost spice due to the specific conditions for optimal growth and because of being harvested by hand. The massive income from commercializing saffron substituted with other plant parts or low-cost spices makes this spice the main target of fraudsters. Background: Different methods have been developed for detecting saffron adulteration. Most of them are time consuming and complex, and in some types of analysis, the whole untargeted dataset is combined with advanced chemometric tools to differentiate authentic from non-authentic saffron. The official method, combining UV–vis spectroscopy and LC to determine the colour strength and the crocin content, is unable to detect saffron adulterants (safflower, marigold, or turmeric) added at a level lower than 20% (w/w). As a result, innovative approaches based on rapid, high-throughput methods for the identification of adulterated saffron samples are urgently demanded to counteract food frauds. Methods: This paper describes, for the first time, the development of a method combining Direct Analysis in Real Time (DART) with the triple quadrupole MS EVOQ based on the detection of specific MS/MS transitions, promoting a rapid, robust and chromatography-free method capable of monitoring safflower and turmeric adulteration in saffron. Results: The method proved to reach low LODs, allowing the determination of tiny amounts of turmeric and safflower powder in saffron as low as 3% and 5%, respectively, speeding up the whole analytical workflow and enabling us to perform 20 analyses in 10 min. Finally, the greenness of the method was also assessed according to the 0.88 score achieved by submitting it to the greenness calculator AGREE. Conclusions: Given its speed, simplicity, and robustness, this method stands out as a strong candidate for routine implementation in testing laboratories as a rapid screening tool to detect saffron adulteration with safflower or turmeric. Full article

This study investigates the long-run impact of digital transformation and fintech innovation on environmental sustainability across OECD countries from 1999 to 2024. Drawing on a novel empirical framework that integrates panel fully modified ordinary least squares, the system-generalized method of moments, and machine learning estimators, the analysis captures both linear and nonlinear dynamics while addressing heterogeneity, endogeneity, and structural complexity. Environmental sustainability is measured by per capita CO₂ emissions, while digital transformation and fintech innovation are proxied by secure internet servers and G06Q patent applications, respectively. The findings reveal that both digital infrastructure maturity and fintech-driven innovation significantly reduce carbon emissions, suggesting that technologically advanced digital ecosystems serve as effective instruments for climate mitigation. Robustness checks via the system-generalized method of moments confirm the stability of these relationships, while machine learning models—Random Forest and XGBoost—highlight digital variables as top predictors of emissions reduction. The convergence of results across estimation methods underscores the reliability of the digital–environmental nexus. Policy implications emphasize the need to embed sustainability metrics into digital strategies, promote green fintech regulation, and prepare labor markets for Industry 4.0 transitions. These findings position digital and fintech innovation not merely as enablers of economic growth, but as structural levers for achieving environmentally sustainable development in high-income economies. Full article

Addressing climate change requires a deeper understanding of all greenhouse gases, yet nitrous oxide (N₂O)—despite its significant global warming potential—remains underrepresented in sustainability analysis and policy discourse. The paper examines N₂O emissions from an environmental, social, and governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors on emissions, with intricate interdependencies between economic growth, resource productivity, and environmental policy. Econometric specifications identify forest degradation, energy intensity, and income inequality as the most significant determinants of N₂O emissions, which are in need of policy attention. Machine learning enhances predictive power insofar as emission drivers and country-specific trends are identifiable. Through the integration of panel data techniques and state-of-the-art clustering algorithms, this paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of this study are that while developed nations have better energy efficiency and environmental governance, they remain significant contributors to N₂O emissions due to intensive industry and agriculture. Meanwhile, developing economies with energy intensity have structural impediments to emission mitigation. The paper also identifies the contribution of regulatory quality in emission abatement in that the quality of governance is found to be linked with better environmental performance. ESG-based finance instruments, such as green bonds and impact investing, also promote sustainable economic transition. The findings have the further implications of additional arguments for mainstreaming sustainability in economic planning, developing ESG frameworks to underpin climate targets. Full article

Facing the widespread cooperation among different agribusiness entities in China, this study explores the impact mechanism of land scale on farmers’ cooperation with new agricultural business entities (abbreviated as NABEs), including family farms, cooperatives, and agribusinesses. The effects of income within the cooperation mechanism are further analyzed. Based on survey data from 1558 farmers in 10 provinces, applying binary Logit regression and mediation effect models, the study finds the following: (1) The current land area, past growth of land, and future willingness to expand land all positively affect farmers’ cooperation with new agricultural business entities; (2) An inverted U-shaped relationship exists between land size and the proportion of farmers joining new agricultural business entities. The probabilities of joining family farms, cooperatives, and agribusinesses peak at land sizes of 2.65, 6.82, and 7.04 acres, respectively; (3) The current income situation has an intermediary effect on the cooperation between farmers and family farms, while the future income expectation has an intermediary effect on the cooperation between farmers and cooperatives and agribusinesses; (4) The effect of land scale on cooperation is more significant for farmers of village officials or agricultural organization members, full-time farmers, and those with green production and modern sales. This study proposes a development growth curve of farmers, which can be divided into “self-development–cooperation–transformation” stages, and gives solutions for each stage, to facilitate moderate-scale operations and long-term cooperation among various entities in the context of market reforms and social division of labor. Full article

The increasing risks posed by climate change have turned CO₂ emissions into a pressing global issue, prompting the widespread adoption of sustainable development policies. This study investigates the empirical drivers of CO₂ emissions across 15 of the world’s highest carbon-emitting countries from 2000 to 2021, using a range of advanced panel data techniques. The core explanatory variables include green energy transition (GET), fossil fuel consumption (FFC), financial development (FD), mineral resource consumption (MRC), energy intensity (EI), and information and communication technology (ICT). By employing cross-sectional dependence tests, CIPS and CADF unit root tests, cointegration techniques (Westerlund and Dickey-Fuller), and Driscoll-Kraay standard error (DKSE) estimators, the study ensures robust and reliable inference. The findings reveal that a 1% increase in GET and FD leads to a 1.59% and 4.51% decrease in CO₂ emissions, respectively, while higher energy efficiency (EI) also significantly reduces emissions. In contrast, greater use of fossil fuels, mineral resources, and ICT expansion contributes positively to emissions. These results demonstrate the critical role of financial systems, clean energy investments, and energy efficiency in mitigating environmental degradation. The study offers targeted policy insights for countries aiming to balance economic growth with climate goals and highlights the need for enhanced technology transfer and financing mechanisms in low- and middle-income countries. Full article

Copper and zinc are metals commonly used in industry. However, improperly disposed copper and zinc pollute soil seriously. In farmland where the concentrations of copper and zinc exceeded regulatory standards and farming has been banned for many years, we measured the copper and zinc concentrations in soil. The copper concentration ranged from 30.2 to 1082.3 mg/kg, while the zinc concentration was between 200.2 and 3335.3 mg/kg. To explore the correlation between the concentration of copper and zinc in soil and plants and plant growth, Pennisetum was chosen as the test crop. The economic and carbon reduction benefits of planting Pennisetum in copper- and zinc-polluted farmland were also investigated. The results indicated that the concentration levels of copper and zinc were not significantly impacted, and neither was the growth of Pennisetum. Farming Pennisetum produces a total of about 1100 tons of biomass per hectare per year. The income per hectare was about USD 48,000 per year. Pennisetum captures 578.8 tons of carbon every year, equivalent to 2124.2 ton-CO₂e. When used as fuel, it provides 23,649 GJ of bioenergy. Therefore, Pennisetum is an appropriate plant for the green and sustainable remediation of polluted soil. Full article

This study explores the dynamic relationship between economic growth, technological innovation, and $C{O}_2$ emissions in BRICS nations, integrating the Environmental Kuznets Curve (EKC) and Innovation Claudia Curve (ICC) frameworks. Using a panel ARDL approach on data from 1991 to 2023, we investigate the long-run and short-run interactions between GDP, renewable energy consumption (RENC), foreign direct investment (FDI), urbanization (URB), and patent applications (PAs) in shaping environmental outcomes. The findings confirm the EKC hypothesis, revealing an N-shaped relationship between GDP and emissions, indicating that while economic growth initially leads to higher $C{O}_2$ emissions, this trend reverses at a critical threshold before a secondary increase occurs at higher income levels. The ICC framework identifies a cubic relationship between innovation and emissions, where technological advancements initially drive higher emissions before contributing to sustainability at later stages, though an excessive scale of innovation may reintroduce environmental pressures. RENC is found to significantly mitigate emissions, while URB and FDI display dual and context-dependent effects, highlighting the multidimensionality of sustainable transitions in emerging economies. These results underscore the importance of targeted policy interventions, such as scaling renewable energy infrastructure, promoting green innovation, guiding urban expansion, and aligning FDI with environmental objectives. Full article

Assessing rural habitat suitability and its connection to land response is a vital tool for understanding the socio-economic and environmental challenges in rural areas tailored to local contexts. This study fills existing research gaps by examining the suitability of rural habitats in Anhui Province, opening pathways to reveal how rural sustainability may connect to land. Using the autoregressive distributed lag (ARDL) model, it analyzes the short- and long-term effects of socio-economic and environmental factors on rural suitability across various counties. Additionally, a descriptive analysis explores the pathways linking rural suitability to land use responses. The findings reveal that rural greening, village planning, and housing area per resident positively influence rural habitat suitability in both the short and long term. However, agricultural income growth shows a negative impact, potentially due to structural issues in the sector. Environmental factors like temperature and rainfall have a limited influence on rural suitability. The study underscores the importance of suitable rural infrastructure, namely enhancing rural greening rate, implementing village plans, and improving housing for sustainable rural development. Regional variations in rural habitat suitability across Anhui Province are also evident. While some cities, such as Huaibei and Anqing, demonstrate success in revitalization, others, like Suzhou and Chizhou, face challenges. The results highlight the need for region-specific strategies that account for local environmental, economic, and infrastructural contexts. Tailored approaches are essential to achieving long-term, effective rural development in the province. Full article

Investigating the spatio-temporal trends in agricultural green production level and proposing pathways to improve it can offer valuable insights for promoting the green, low-carbon, and sustainable development of China’s agriculture, as well as contributing to the achievement of the United Nations’ Sustainable Development Goals by 2030. Therefore, in order to investigate the spatio-temporal variations in agricultural green production level and its driving factors, and explore pathways to improve it in the major grain-producing areas of China, a new multi-dimensional framework for estimating the agricultural green production level was proposed, and based on the OLS regression and scenario prediction, the agricultural green production levels from 2012 to 2030 were estimated. The findings indicate that from 2012 to 2021, the agricultural green production level in the major grain-producing areas experienced a consistent annual increase. The average annual value for the agricultural green production level was recorded at 0.443. At a spatial scale, the agricultural green production level exhibited a pronounced regional pattern, showing higher levels in the central and eastern areas, while lower levels were noted in the northeastern and western regions. The actual utilization of foreign capital and the per capita disposable income of farmers positively influenced the agricultural green production level. In contrast, factors such as the proportion of the secondary industry, the proportion of the tertiary industry, and the urbanization rate negatively affected this level. From 2022 to 2030, the agricultural green production level is expected to demonstrate a gradual growth trend under the baseline scenario, although the rate of growth is expected to decrease over time. Conversely, under the green and sustainable development scenario, a notably significant growth trend in agricultural green production level is projected. However, under the rapid economic development scenario, it is estimated that the agricultural green production level will initially increase slowly before peaking in 2026 and then experiencing a decline. With the aim of ensuring the ongoing enhancement of agricultural green production level objectives, the major grain-producing areas should proactively encourage inter-provincial collaboration in agricultural green production, vigorously attract foreign investment to facilitate the advancement of green production technologies, promote the harmonious integration of primary, secondary, and tertiary industries in rural regions, and improve farmers’ income. Full article

All nations are searching for ways to address their environmental gaps to assure long-term sustainability, given the alarming rate at which the environment is deteriorating. As one of the nations pursuing clean energy, Oman needs to embrace eco-friendly practices that can encourage sustainability and resource efficiency to establish green ecosystems. This study uses an autoregressive distributed lag (ARDL) model to examine the link between CO₂ emissions, GDP, energy consumption, financial development, foreign direct investment, urbanization, and population in the Sultanate of Oman between 1990 and 2023. The Middle Eastern nation of Oman was selected for the case study because it has traditionally depended on its domestic fossil fuel resources. Furthermore, the country has been a net exporter and surplus oil producer, underscoring Oman’s long-standing reliance on fossil fuels. The findings indicate that urbanization and GDP lower CO₂ emissions, whereas population growth, energy use, FDI, and financial development raise emissions. As per the EKC model, the GDP2 coefficient was 0.488 and β1 < 0. This suggests that there is a positive correlation between environmental degradation and economic growth in Oman, although the EKC only applies up to a particular income level. The findings suggest enacting additional environmental regulations to support sustainable business behavior, raising public understanding of environmental issues, using more clean energy technologies, lowering energy consumption, and reaching the goal of net-zero carbon emissions. Full article

The rapid increase in urban population density driven by urban development has intensified inequity in urban green space distribution. Identifying the causes of changes in green equity and developing strategies to improve urban greening are crucial for optimizing resource allocation and alleviating social inequalities. However, the long-term spatio-temporal evolution of green visibility and equity remains underexplored. This study utilized the “Time Machine” feature to capture street view images from 2014, 2017, and 2021, analyzing changes in green visibility and its equity across residential communities in Wuhan. Deep learning techniques and statistical methods, including the Gini coefficient and location quotient (LQ), were employed to assess the distribution and spatial equity of street-level greenery. The results showed that overall green visibility in Wuhan increased by 4.18% between 2014 and 2021. However, this improvement did not translate into better spatial equity, as the Gini coefficient consistently ranged between 0.4 and 0.5. Among the seven municipal districts, only the Jiang’an District demonstrated relatively equitable green visibility in 2017 and 2021. Despite a gradual reduction in disparities in green visibility, a spatial mismatch persisted between UGS growth and population distribution, leading to uneven patterns in UGS equity. This study explores the factors driving inequities in green visibility and proposes strategies to enhance urban greening. Key recommendations include integrating the green visibility equity evaluation framework into urban planning to guide fair green space allocation, prioritizing greenery in low-income neighborhoods, and reducing hardscapes to support the planting and maintenance of tall canopy trees. These measures aim to enhance accessible and visible green resources and promote equitable access across communities. Full article

In many countries, the urban–rural income inequality affects healthy and sustainable economic development and is a pressing issue that requires immediate attention. As a new industrial development model, rural industrial convergence can provide new ideas and impetus for alleviating the urban–rural income inequality. This study, drawing on provincial panel data from China spanning 2010 to 2022, used the entropy method and Theil index to measure the rural industrial convergence and the urban–rural income inequality, respectively, and empirically tested the effect and mechanism of rural industrial convergence on the urban–rural income inequality. The results showed the following: (1) Rural industrial convergence had a notable impact on alleviating the urban–rural income inequality. (2) Rural industrial convergence could help reduce the urban–rural income inequality by increasing the scale of land operation. (3) The government attention to green development could positively moderate the impact of rural industrial convergence on the urban–rural income inequality; the deeper the government attention to green development, the greater the role rural industrial convergence played in alleviating the urban–rural income inequality. (4) There was a threshold effect in the alleviating effect of rural industrial convergence on the urban–rural income inequality, which was gradually strengthened when the growth of the digital economy and the enhancement of the business environment were beyond the threshold point. (5) Rural industrial convergence also had significant spatial spillover effects on adjacent regions. Overall, the findings of this study enrich the research on the impact of rural industrial convergence on the urban–rural income inequality and provide insights for other similar countries. Full article