Environmental Protection Research Articles

From corporate social responsibility to customer satisfaction: A study of repurchase intention in Malaysian petroleum industry

Corporate social responsibility (CSR) is an important concept of modern economic theory. In the last few decades, it has become an increasingly popular marketing tool used by companies. Consumers too want to see more CSR activities, especially those focused on environmental protection. The petroleum industry produces both toxic and non-toxic waste at almost all stages of production. While petroleum companies satisfy market demand, they also want to meet consumers’ moral and ethical demands. In this light, CSR has become vital for the development of industry. This paper looks at CSR in the petroleum industry, and its effect on customer satisfaction and subsequently toward the customer repurchase intention in Malaysia. The starting point of this paper is the Stakeholder Theory. It then examines CSR endeavors within the oil and gas sector and its link to customer repurchase intentions. It also looks at the established hypotheses between the activities of CSR (Economic Responsibility, Legal Responsibility, Ethical Responsibility, Philanthropic Responsibility), customer satisfaction and repurchase intention. This paper aims to learn about the customer’s sense of fulfilment with the CSR activities, and what could be the reaction base on the customer’s expectation.

Dynamic Modeling of Soil Water Dynamics and Nitrogen Species Transport with Multi-Crop Rotations Under Variable-Saturated Conditions

Excessive application of nitrogen (N) fertilizers in agriculture poses significant environmental risks, notably nitrate leaching into groundwater. This study evaluates soil water dynamics and the transport of urea, ammonium, and nitrate under variable-saturated conditions in a long-term experimental field in Croatia, Europe. Utilizing HYDRUS-1D and HYDRUS-2D models, we simulated water flow and nitrogen transformation and transport across six lysimeter-monitored locations over four years (2019–2023), incorporating diverse crop rotations and N addition. Key modeled processes included nitrification, urea hydrolysis, and nitrate leaching, integrating field-measured parameters and climatic conditions. The models achieved high reliability, with R2 values for water flow ranging from 0.58 to 0.97 and for nitrate transport from 0.13 to 0.97; however, some cases reported lower reliability. Results revealed that nitrate leaching was influenced by precipitation patterns, soil moisture, crop growth stages, and fertilization timing. Peak nitrate losses were observed during early crop growth and post-harvest periods when elevated soil moisture and reduced plant uptake coincided. The findings highlight the importance of optimizing nitrogen application strategies to balance crop productivity and environmental protection. This research demonstrates the effectiveness of numerical modeling as a tool for sustainable nitrogen management and groundwater quality preservation in agricultural systems. It also indicates the need for further development by capturing some of the processes such as identification in the N cycle.

Energy Efficiency in Greenhouses and Comparison of Energy Sources Used for Heating

Sustainability in greenhouse farming, one of the areas where the most energy is needed in the agricultural sector, can be achieved by increasing energy efficiency. Due to increasing energy costs in Türkiye and worldwide, increasing energy efficiency in greenhouses is seen as possible using renewable energy sources that do not produce waste instead of fossil energy sources. This study determined the heat-energy demand in the provinces of Türkiye with continental (Kırşehir and Kütahya) and Mediterranean (Antalya and Mersin) climates. For this purpose, the heat-energy requirement was calculated for greenhouse types with three different insulation properties (S-1: roof and side walls polyethylene, S-2: roof polyethylene, side walls polycarbonate, and S-3: roof polyethylene, side walls polycarbonate, and thermal curtain). Then, the amount and cost of fossil (coal, fuel oil, and natural gas) and renewable energy sources (geothermal and biogas) to be used in obtaining this energy, the heating cost for unit tomato yield, and the amount of carbon dioxide (CO2) released into the atmosphere were compared. According to the results obtained, the highest heat-energy requirement was 356.5 kWh m−2 year−1 in the S-1 greenhouse in the Kütahya province, and the lowest was 46.3 kWh m−2 year−1 in the S-3 greenhouse in the Mersin province. Depending on energy conservation, 6% of energy savings can be achieved in S-2 and 29% in S-3 compared to S-1. The highest heating cost for producing one kilogram of tomatoes was 0.70 USD kg−1 in fuel oil and Kütahya province (S-1). The lowest was calculated as 0.06 USD kg−1 in geothermally heated greenhouses in Kırşehir and Kütahya provinces (S-3). The highest CO2 to be released into the atmosphere with fuels was equal to 253.1 kg m−2 year−1 in coal fuel in Kütahya province (S-1). The lowest was calculated as 1.1 kg m−2 year−1 in geothermally heated greenhouses in Kırşehir and Kütahya provinces (S-3). The results of this research can be used to develop feasibility studies for greenhouse companies, greenhouse sector policies, policymakers, environmental protection, and taking precautions against the climate crisis.

Understanding Undervaluation: A Law and Psychology Analysis of the WTO Environmental Goods Agreement

Recent research in environmental law and psychology has revealed a pernicious problem: psychological phenomena often lead people to underperceive and undervalue environmental harms. These insights have yet to be applied to the transnational realm. This article employs a psychological approach to explore the evaluation of environmental concerns in the context of the World Trade Organization’s Environmental Goods Agreement (“EGA”). Although the EGA purports to be a “win-win” agreement that simultaneously promotes economic development and environmental protection, it has failed to reach meaningful agreement. The present research argues that a psychological perspective on environmental law may supplementally explain the failure in EGA negotiation by demonstrating how states inadvertently undervalue the transboundary environmental impacts of environmental goods. This insight also fills the void left by rational choice theory in regard to the valuation process.

Construction and Optimization of Ecological Network in Kuqa, China

The ability to achieve coordinated progress towards ecological protection has become an important ecological network, is the basic guarantee, and an important way to achieve regional and urban ecological security. This study takes Kuqa City as the research area, using the Morphological Spatial Pattern Analysis (MSPA) method and the landscape connectivity index to extract the ecological source area. After establishing the comprehensive resistance surface, the Minimum Cumulative Resistance (MCR) model is used to identify the ecological corridor, and the source area and the corridor are combined to construct the ecological network of the study area. The analysis results show that the area of cropland continued to rise in 2020, and the area of woodland and grassland decreased; the overall analysis of the ecological security pattern yielded good results, and the main influencing factors were terrain and altitude. We extracted eight ecological source areas, and the distribution structure of the source sites formed two large source site communities in the north and south, with a clear break in the middle. After establishing a comprehensive resistance surface, 28 general corridors were identified using the MCR model. Corridors are more scarce in the western part of the study area, so seven important corridors have been identified according to the gravity model, and four new sources have been added to expand the ecological network coverage area, which can solve the fault phenomenon between the southwest and middle east. Constructing a reasonable ecological network can significantly improve the landscape connectivity of the entire study area, which is conducive to promoting the construction of ecological civilization in Kuqa City, and is of great significance to sustainable development and the protection of the ecological environment.

The Impact of Environmental Subsidies and Enforcement on Green Innovation: Evidence from Heavy-Polluting Enterprises in China

This paper investigates the critical role of government policies in promoting green development through enterprise-level green innovation (GI). We specifically examine the impact of environmental subsidies (ES) on heavily polluting enterprises and analyze how government environmental enforcement (EE) moderates this relationship. Furthermore, we explore how these effects vary across different ES characteristics and allocation patterns. Using data from heavy-polluting enterprises listed on China’s A-share market from 2012 to 2021, our analysis reveals several key findings. First, ES has unexpectedly hindered GI in heavily polluting enterprises. This negative effect stems from both ES characteristics and allocation patterns. Specifically, advance subsidies, targeted subsidies, and pollution prevention subsidies prove ineffective. The adverse impact of ES on GI is particularly pronounced in state-owned enterprises, companies lacking environmental protection investments, and firms engaging in earnings management practices. However, our findings indicate that strong environmental enforcement (EE) can effectively mitigate these ES-related issues. Under strict EE, ES demonstrates a positive effect on GI and addresses problems associated with both ES design characteristics and allocation patterns. These results offer valuable insights for government policymakers, particularly in developing countries undergoing green transition, to design and implement more effective environmental policies.

A New Multi-Criteria Decision-Making Tool for Subsea Oil and Gas Asset Decommissioning

Abstract The decommissioning of offshore oil and gas (O&G) facilities poses significant challenges due to high costs, operational risks, social impacts, and environmental protection concerns. Multi-criteria decision analysis (MCDA) emerges as a vital tool to navigate these complexities, enabling stakeholders to evaluate diverse decommissioning alternatives against a broad set of criteria. Existing literature lacks extensive documentation on the application of MCDA in decommissioning decisions, particularly in quantitatively evaluating the performance of different decommissioning strategies against a comprehensive set of criteria. This study introduces a novel MCDA model tailored for the decommissioning of subsea O&G assets, aiming to improve decision-making by providing a quantitative ranking of decommissioning alternatives. Employing the Preference Ranking Organization Method for Enrichment Evaluations method, the model considers 37 attributes across six criteria (safety, environment, waste management, technical, social, and economic) to evaluate several decommissioning alternatives for the Brazilian Espadarte Field. Weightings and scenarios are defined through expert consultation, enhancing the model’s adaptability and relevance to real-world contexts. The model’s application demonstrated significant efficacy, with the selected top-ranked decommissioning alternative achieving a performance indicator of 58%, corresponding to the removal of 50% of the pipeline length considered. This result underscores the model’s capacity to identify optimal compromises between competing criteria. The study contributes to the field by filling the gap in the quantitative evaluation of decommissioning alternatives, offering a robust, transparent decision-making tool that accommodates complex, multi-criteria decisions. The model provides a significant advancement in the rationalization of decommissioning strategies for O&G assets.

Spatio-Temporal Dynamic Impacts of Land Use/Cover Change on Eco-Environment Quality in Li River Basin, China

Clarifying the spatio-temporal evolution characteristics of eco-environment quality (EEQ) under land use/cover change (LUCC) and its coordinated relationship is of great importance for formulating reliable environmental protection strategies and measures to promote regional sustainable development. Most studies have emphasized the importance of LUCC for regional ecological quality. However, deeply unraveling the complex interrelationships between them remains a significant challenge, particularly in ecologically fragile regions like the Li River Basin. Therefore, based on the historical land use data and the remote sensing ecological index (RSEI) of the Li River Basin from 1990 to 2020, we analyzed the spatio-temporal evolution characteristics of EEQ and LUCC, and explored the influences and non-linear effects between them by using the bivariate spatial autocorrelation and XGBoost model. The key findings are as follows: (1) Land use/cover (LUC) in the Li River Basin was predominantly characterized by forestland and cropland, which together accounted for approximately 97% of the region. The interconversion between forestland and cropland represented the primary form of regional LUCC, while built-up land demonstrated a growth trend by encroaching on cropland. (2) The EEQ exhibited a volatile upward trend within the research period, with an average RSEI value of 0.5891, indicating a generally favorable ecological condition. (3) A significant negative spatial correlation was observed between land use intensity (LUI) and the RSEI, characterized by H–L, L–H, and non-significant clusters. (4) There was a distinct non-linear relationship that existed between LUCC and the RSEI, underscoring that appropriately regulating regional land use scale can help maintain ecological balance. These findings provide a scientific basis for optimizing land spatial management models and formulating policies to improve ecological environment quality, while also offering a new framework and reference for further ecological research on EEQ influencing factors and driving mechanisms.

Improving Groundwater Quality Through Biosphere Reserve Management: Insights from the Anaga Reserve, Tenerife

The Canary Islands, an outermost Spanish territory in the Atlantic Ocean, are renowned for their subtropical climate and significant tourism. However, substantial areas are designated for environmental protection, notably the Anaga Rural Park in Tenerife, a UNESCO Biosphere Reserve, which is the focus of this study. This research investigates the influence of Biosphere Reserve designation on groundwater quality, a crucial resource for Tenerife’s population. We analysed the physicochemical properties of groundwater within the Anaga region over a decade (2007–2016). Our findings demonstrate that groundwater quality consistently meets regulatory standards, exhibiting no evidence of pollution. This high quality is attributed to several factors, including the low population density, limited tourism impact within the reserve, and crucially, the effective soil protection measures implemented within the Biosphere Reserve. The compact geology of the region further limits infiltration and potential pollution. The sustained high quality of groundwater, even in the absence of detectable pollution, highlights the importance of ongoing monitoring to maintain this valuable resource and support local biodiversity. This case study provides a valuable model for sustainable groundwater management and soil protection strategies in other areas of Tenerife and beyond.

Digital transformation, green technology innovation and corporate value

One of the key factors influencing corporate sustainable development is green technology innovation (GTI). Our study intends to explore how digital transformation impacts corporate value through GTI. We use panel data from A-share publicly listed firms in China spanning from 2012 to 2022 as our sample. We employ textual analysis to extract keywords correlated to digital transformation from the annual reports, and construct an indicator to evaluate the digital transformation of corporates. Our findings show that digital transformation significantly enhances enterprise value by improving green technology innovation. Furthermore, the market competition and green credit moderate the mediated relationship between digital transformation and corporate value. Additionally, heterogeneity tests indicate that digital transformation has a more significant influence on value enhancement for heavily polluting corporates, non-high-tech corporates, and smaller corporates. This research offers important recommendations for practitioners on advancing sustainable business practices and provides policy recommendations for environmental protection and green development.

Impact of Recycled Fine Aggregate on Physical and Mechanical Properties of Green Mortar

Through research that combined green environmental protection with the resource usage of solid waste, we explored more possibilities for mortar using recycled fine aggregate (RFA) as a material. In this work, natural fine aggregate (NFA) with different proportions of RFA in mortar was produced, while maintaining the same particle size distribution. Four types of mortar were produced, with replacement ratios of 25%, 50%, 75%, and 100%, as well as a reference mortar type without RFA. A comprehensive evaluation of the mortar with different proportions was conducted, including its workability, density, capillary water absorption, compressive strength, and flexural strength. The results indicated that the compressive strength and flexural strength of mortar containing 50% RFA improve within 14 days. In addition, with increased RFA usage, the mortar’s mechanical properties decreased. The data obtained from this study will help in the application of RFA in green mortar.

Transportation-Associated Carbon Dioxide Emissions Avoided by Use of Telehealth Through COVID-19 Pandemic.

Background: Increasing carbon dioxide (CO2) in the atmosphere contributes to Earth's warming, which has negative impacts on human health. The health care system is a major contributor to CO2 emissions. Telehealth has the capacity to reduce health-care-related emissions by eliminating patient travel to in-person appointments. Methods: Data were obtained from Nebraska Medicine's (NM) electronic medical record. Parameters included patient zip code, provider location, calendar year of visit, and provider specialty. Euclidean distance from centroid zip code to clinic location was calculated. Environmental Protection Agency estimates were used to convert mileage to CO2 saved. Results: During the period January 1, 2019, to January 31, 2022, the NM health care system completed 214,241 telemedicine visits for patients whose home zip code is within Nebraska, resulting in greater than 2,600 metric tons of CO2 avoided. Telehealth appointments increased by more than 22,000% from 2019 to 2020. An average of 12.38 kg of CO2 was avoided with each telehealth visit. Medical specialties that avoided the most CO2 included family medicine, endocrinology, and infectious disease. Conclusion: This study demonstrates the capability of the NM health care system to rapidly adjust to an emergency pandemic by drastically increasing the use of telehealth, which also avoided thousands of tons of transportation-associated CO2 emissions. Telehealth appointments increased during the height of the pandemic by more than 22,000%. Telehealth is an effective CO2 emission-reducing strategy and a worthy avenue to further explore reduced health-care-related emissions.

Retraction Note to: Examining the Impact of China’s New Environmental Protection Law on Enterprise Productivity and Sustainable Development

Retraction Note to: Examining the Impact of China’s New Environmental Protection Law on Enterprise Productivity and Sustainable Development

Demand Forecasting and Allocation Optimization of Green Power Grid Supply Chain Based on Machine Learning Algorithm: A Study Based on the Whole-Process Data of Power Grid Materials

The efficient management of the green power grid supply chain is of great significance in addressing global energy transformation and achieving sustainable development goals. However, traditional methods struggle to effectively cope with the complexity and dynamics of demand forecasting and the multi-objective optimization problems in material allocation. In response to this challenge, this paper proposes a machine-learning-based demand forecasting and allocation optimization method, aiming to improve the management efficiency of the supply chain and reduce environmental impacts. First, based on the whole-process data of power grid materials, a multi-model fusion strategy is adopted for demand forecasting. By combining machine learning models such as long short-term memory (LSTM), extreme gradient boosting (XGBoost), and random forest, the prediction accuracy and the generalization ability of the model are significantly improved. Moreover, a “distributed collaborative optimization algorithm” is proposed. By decomposing the power grid regions, this paper optimizes transportation routes and inventory management, and comprehensively reduces transportation, inventory, and environmental protection costs while taking into account the real-time requirements in a complex supply chain environment. Finally, an empirical analysis is carried out in combination with the optimized allocation plan, verifying the practical effectiveness of the proposed method. The results indicate that the optimized scheme significantly outperforms the traditional method in terms of total cost, transportation efficiency, and carbon emissions. Specifically, the optimized scheme achieves a 13% reduction in transportation costs, a 10% decrease in inventory costs, and a 25% cut in environmental protection expenses. Additionally, it decreases transportation-related carbon emissions by approximately 250 tons. The demand forecasting and allocation optimization method based on machine learning has obvious economic and environmental advantages in the green power grid material supply chain. By effectively integrating various algorithms, this paper enhances the accuracy and stability of material management while substantially reducing operating costs and carbon emissions. This is in line with the sustainable goals of green power grid development. The paper provides an optimized framework with practical value for managing the green supply chain in the power grid industry.

DeiT and Image Deep Learning-Driven Correction of Particle Size Effect: A Novel Approach to Improving NIRS-XRF Coal Quality Analysis Accuracy

Coal, as a vital global energy resource, directly impacts the efficiency of power generation and environmental protection. Thus, rapid and accurate coal quality analysis is essential to promote its clean and efficient utilization. However, combined near-infrared spectroscopy and X-ray fluorescence (NIRS-XRF) spectroscopy often suffer from the particle size effect of coal samples, resulting in unstable and inaccurate analytical outcomes. This study introduces a novel correction method combining the Segment Anything Model (SAM) for precise particle segmentation and Data-Efficient Image Transformers (DeiTs) to analyze the relationship between particle size and ash measurement errors. Microscopic images of coal samples are processed with SAM to generate binary mask images reflecting particle size characteristics. These masks are analyzed using the DeiT model with transfer learning, building an effective correction model. Experiments show a 22% reduction in standard deviation (SD) and root mean square error (RMSE), significantly enhancing ash prediction accuracy and consistency. This approach integrates cutting-edge image processing and deep learning, effectively reducing submillimeter particle size effects, improving model adaptability, and enhancing measurement reliability. It also holds potential for broader applications in analyzing complex samples, advancing automation and efficiency in online analytical systems, and driving innovation across industries.

Recent Progress in Intrinsically Stretchable Sensors Based on Organic Field-Effect Transistors

Organic field-effect transistors (OFETs) are an ideal platform for intrinsically stretchable sensors due to their diverse mechanisms and unique electrical signal amplification characteristics. The remarkable advantages of intrinsically stretchable sensors lie in their molecular tunability, lightweight design, mechanical robustness, solution processability, and low Young’s modulus, which enable them to seamlessly conform to three-dimensional curved surfaces while maintaining electrical performance under significant deformations. Intrinsically stretchable sensors have been widely applied in smart wearables, electronic skin, biological detection, and environmental protection. In this review, we summarize the recent progress in intrinsically stretchable sensors based on OFETs, including advancements in functional layer materials, sensing mechanisms, and applications such as gas sensors, strain sensors, stress sensors, proximity sensors, and temperature sensors. The conclusions and future outlook discuss the challenges and future outlook for stretchable OFET-based sensors.

ENVIRONMENTAL LICENSING IN COMPARATIVE PERSPECTIVE

This article analyzes environmental licensing as an essential instrument for environmental protection, comparing the models adopted in Spain, France, Portugal and the United States. The research, using a comparative approach, evaluates the practices and legislation of these countries, identifying good practices and challenges. It is concluded that the effectiveness of licensing depends on adaptation to local specificities and the ability to balance environmental protection and economic development. This study provides support for improving regulatory systems in different contexts.

OPTIMIZATION OF PARAMETERS OF SOLAR COLLECTORS CONCENTRATORS FOR GREEN BUILDINGS

The use of solar collectors with solar concentrators in green buildings is an important step in the direction of creating energy-efficient and environmentally friendly buildings. These technologies make it possible to provide a sustainable source of energy, reduce emissions and save resources. Considering the positive aspects of using solar collectors, their implementation in construction can become an important step in the development of sustainable construction and environmental protection. The use of solar collectors with concentrators can significantly reduce the consumption of electricity and other energy sources, which is an important aspect for green buildings. These collectors are able to efficiently collect solar energy and concentrate it for further use in various heating, cooling and power generation systems. Increasing the energy efficiency of green buildings is possible due to the use of solar systems with optimal orientation parameters that use renewable, environmentally friendly solar energy for the energy supply of buildings. An analytical method has been developed to determine the optimal parameters - the angle of inclination of the solar collector reflector to obtain the maximum amount of converted solar energy, taking into account the number of glass layers, their transparency and absorption coefficient depending on the angle of incidence of reflected solar rays, shading by the collector frame, etc. Calculations were made and optimal angles of the reflector were determined for single-layer and double-layer glazing of the collector. The proposed method can be used in the design of green buildings that use solar energy for energy supply.

Tracking the Green Transition in the European Union Within the Framework of EU Cohesion Policy: Current Results and Future Paths

Green transition is one of the priority areas in the European Union (EU), and thus also the focus of EU cohesion policy implementation during the 2021–2027 programming period, through “green growth”. To establish adequate guidelines and achieve better outcomes, it is necessary to observe and evaluate current results connected with these aspects. In terms of the complexity of the green transition domains, measuring green transition is still developing. The aim of this paper is to give an overview of the approaches that can be used in tracking the green transition as a result of EU cohesion policy implementation and to analyse achievements associated with the green transition. This will be done using desk research, previous studies, documents, evaluation reports and secondary data on the financing under the European Structural and Investment Funds (ESIFs) in EU member states, with a special emphasis on themes such as the low-carbon economy, climate change adaptation and risk prevention, environment protection and resource efficiency, and network infrastructure in transport and energy. The paper discusses these results in the context of the current challenges faced by the EU in becoming a leader in the green transition and contributes to the evaluation of the EU cohesion policy.

Mitigating risks in public procurement

Purpose This study aims to examine the challenges and risks in public procurement, focusing on Greece’s context within the EU framework. It seeks to provide guidance on mitigating potential risks throughout different stages of public procurement by leveraging the knowledge of Greek professionals and existing literature. The research explores the implementation of the national public procurement plan in Greece, aiming to accelerate contract awards, reduce bureaucracy, promote competition and encourage cost-effectiveness among contracting authorities. Design/methodology/approach The study uses a qualitative approach, combining a literature review with expert interviews. Greek professionals with extensive experience in public procurement were consulted to gather insights on current practices, challenges and risk mitigation strategies. The research also analyzes EU public procurement strategies and their implementation in Greece. This mixed-method approach allows for a comprehensive understanding of the public procurement landscape, integrating theoretical frameworks with practical insights from industry experts. Findings The research reveals significant improvements in Greece’s public procurement sector despite persistent irregularities and challenges. Key findings include the need for increased transparency, professionalization of sector staff, support for SMEs and enhanced digitization. The study identifies specific risks at various stages of the procurement process and proposes mitigation strategies. It also highlights the importance of implementing the national public procurement plan to streamline processes, reduce bureaucratic hurdles and promote competition and cost-effectiveness. Social implications Effective public procurement management has far-reaching social implications. By improving the quality and efficiency of public spending, it can enhance public services and infrastructure, ultimately benefiting citizens’ quality of life. Increased transparency and fair competition can foster trust between citizens and government, promoting a culture of accountability. Supporting SMEs in public procurement can stimulate economic growth and job creation. Furthermore, promoting sustainability and innovation through procurement practices can contribute to broader societal goals such as environmental protection and technological advancement. Originality/value This study provides a unique perspective on public procurement challenges and risk management in Greece within the EU context. By combining insights from experienced Greek professionals with a comprehensive literature review, it offers practical, context-specific guidance for risk mitigation in public procurement. The research contributes to the growing body of knowledge on public procurement reform in EU member states, particularly those facing similar challenges to Greece. Its findings and recommendations can inform policy-making, improve procurement practices and enhance the overall effectiveness of public spending in Greece and potentially other EU countries.