Environmental Impact Assessment Review最新文献

Addressing energy poverty is crucial for establishing a decarbonized economy that promotes energy equity and shared prosperity, through which the catalytic role of digitalization should be firmly prioritized. The novelty of this study lies in modelling the causal nexus between digitalization and energy poverty and identifying transmission channels. This study validates a negative correlation between digitalization and energy poverty by leveraging a unique household-level dataset, including 2014, 2016, and 2018, underscoring its instrumental role in alleviating energy poverty after performing a Two-Stage Latest Square approach. However, the extent of these mitigating effects varies among household groups according to income, age, and rural-urban disparities. Further mechanism analyses present that digitalization in China alleviates energy poverty by reducing income poverty, enhancing social capital mobility, bolstering the private business sector, and decelerating the trend of an aging population. These findings offer significant insights for emerging and developing economies that aim to tackle energy poverty through digitalization.

Global population growth has long been intertwined with environmental concerns and sustainable development. The building sector, a significant energy consumer worldwide, contributes substantially to total energy consumption and greenhouse gas emissions. With the projected global population projected to reach 9.7 billion by 2050, building energy consumption is expected to rise continuously, emphasizing the need to optimize energy performance for environmental sustainability. Despite efforts by many countries to formulate energy conservation objectives and strategies, empirical evidence reveals a substantial disparity between designed and actual building energy consumption, known as the building energy performance gap (BEPG). This gap poses a significant challenge to energy conservation efforts, and the lack of information integrity is a significant contributor to this gap. To address the challenge of inadequate information integrity in building energy projects, this research proposes an innovative solution based on blockchain technology. By utilizing blockchain's decentralized, transparent, and tamper-resistant nature, the proposed model aims to enhance information transmission mechanisms among stakeholders. Ten key energy-related stakeholders and various information flows within building projects are identified. Based on this, an exploratory architectural information management model incorporating blockchain technology is developed. The model features functionalities such as data recording, retrieval, transmission, and incentive mechanisms to ensure data immutability and reliable access security. Through a case study, the model's performance is evaluated in terms of storage cost, latency, and privacy, demonstrating significant time savings in uploading and transferring files. The proposed blockchain-based model offers a feasible solution to the challenges of inadequate information integrity in building energy projects, promoting collaboration and accurate information transmission. This model contributes to improving project outcomes and advancing sustainable development in the construction industry.

The need for greater citizen participation in EIA is well established, as are the numerous barriers to such participation. Yet there are still important gaps in understanding what political-economic power relations undermine participation. In particular, there are few studies linking (neo)colonialism, neoliberalism and austerity to EIA governance and citizen participation. Moreover, there are comparatively fewer studies of EIA from Latin America, and even less from the Caribbean. In this paper, we contribute to filling these two gaps by studying the connections between structural forms of power of colonial-neoliberal governance, and the experiences of EIA participation in the Caribbean island-nation of Puerto Rico. Despite having been a pioneer in EIA implementation, there are no English-language studies focused on Puerto Rico's EIA process. We find strong limitations to citizen participation in EIA, including lack of access to information, pro-forma participation for previously-made decisions, favoring of private economic interests over sustainability and justice, and a legal framework restricting participation and environmental protection. We argue that these limitations are produced through the colonial neoliberal transformations of environmental governance in Puerto Rico, in particular: policies of extreme deregulation and austerity, a permanent state of emergency and exception, and a highly corrupt corporatocracy.

In epidemiological research, accurate estimation of historical ground-level ozone (O₃) concentrations with enhanced spatiotemporal resolution is crucial for effective exposure assessment. The current state-of-the-art for estimating air pollutant concentrations is a two-stage ensemble method that integrates outputs from multiple machine learning algorithms. Despite its effectiveness, opportunities exist to refine this approach for more precise O₃ estimation. In this study, we propose an enhanced ensemble method that incorporates four key strategies. First, we employ high-resolution spatiotemporal predictors derived from prior machine learning studies for refined secondary learning. Second, we use sophisticated algorithms, including categorical gradient boosting, deep neural network, random forest, stochastic variable Gaussian process, transformer, and a combination of convolutional neural network and long short-term memory neural network, as sublearners to enhance learning capabilities. Third, we spatiotemporally split the sample set and then train submodels separately on each subset to eliminate the unobserved spatiotemporal heterogeneity. Finally, we apply a complex machine learning algorithm, rather than the generalized additive model, for integrating sublearner predictions, enabling the capture of intricate nonlinear relationships beyond basic spatiotemporal linear weights. To validate these improvements, we estimated daily maximum 8-h moving average O₃ concentrations ([O₃]MDA8) across Chinese mainland from 2013 to 2020 at a 1 km spatial resolution. The proposed method demonstrated notable accuracy, achieving an out-of-station determination coefficient (R²) of 0.943 and a root-mean-square error (RMSE) of 10.197 μg/m³. This performance marks a nearly 15% improvement over the best existing Chinese O₃ exposure model based on a single algorithm and also surpasses previous studies utilizing traditional ensemble methods for other air pollutants. Our enhanced ensemble approach significantly bolsters the reliability and robustness of future environmental epidemiological studies by further mitigating “misclassification” errors.

This research seeks to evaluate the critical risks associated with Oil and Gas Construction Projects (O&GCPs) in terms of their level of significance and quantifiable effects on Project Performance (PP) by surveying O&GCPs experts in the USA. A multiphase research methodology was adopted, including Systematic Literature Review (SLR) to identify the key O&GCPs risks; semi-structured interviews to identify PP indicators and establish hypotheses on the effect of O&GCPs risks on PP; surveys to assess the perceived agreement of O&GCPs experts regarding the effects of O&GCPs risks on PP, and to rank the probability and impact of the identified O&GCPs risks; Structural Equation Model (SEM) to quantitatively assess the effects of O&GCPs risks on PP; Fuzzy Set Theory (FST) to assess the level of significance of O&GCPs risks; and interviews to verify the developed assessment models and their outputs. The SLR analysis identified 35 articles on O&GCP risks from 2013 to 2023, revealing 51 risk factors in eight categories based on their sources. The SEM analysis indicated that the oil and gas safety risks category had the strongest direct impact on PP. Furthermore, the FST analysis revealed that non-compliance with PPE emerged as the most significant risk factor across all categories of O&GCP risks. This study enhances knowledge and practice by aiding researchers and practitioners in understanding how key O&GCP risks impact PP. This improved understanding is expected to facilitate the formulation of effective mitigation strategies during the early stages of the projects.

The global mining sector generates billions of tons of tailings stored in thousands of tailing ponds. The occasional spills of tailings resulting from dam failures or pipe damage can have devastating consequences, threatening nearby human populations and ecosystems, particularly those located along river corridors. Satellite remote sensing technology is a vital supplementary method to traditional field methods for monitoring and evaluating the water pollution caused by spilled tailings. The researchers have developed workflows to evaluate the effect of tailing spills on water quality using low and medium-spatial satellite imagery from satellite sensors. Due to insufficient spatial resolution, these workflows were hard to apply to monitor the water pollution caused by spilled tailing in small rivers. Using valuable on-site data from a river water pollution incident caused by spilled tailing, a workflow utilizing high-resolution satellite imagery (GF1) was developed. This workflow incorporates a machine learning algorithm (improved DeepLabV3+) to extract water masks first and a novel spectral index method to determine TSM concentrations. The improved DeepLabV3+ algorithm can obtain an accurate water mask no matter the water pixels, whether influenced by the tailing spills from GF1 imagery with IoU of 82.66%, Precision of 93.21%, Recall of 87.96%, and F1-score of 90.51%. A new spectral index combination algorithm that provides reliable TSM products for an extensive TSM magnitude range was presented to assess the level of water contamination. The strong correlation (R² = 0.97) between in situ TSM and Mo concentrations suggests that the retrieved TSM products are suitable for assessing the water pollution caused by the spilled tailing. This workflow provides a method for monitoring and evaluating water pollution resulting from spilled tailings in small rivers. It utilizes high-resolution satellite data to observe and analyze the pollution levels.

Waste-to-energy (WTE) projects have attracted considerable attention due to their role in addressing waste management issues and promoting renewable energy production. However, while public acceptance of these projects remains controversial, psychological distance (PD) may be a key shaping factor in their construction. This study, grounded in the Construal Level Theory (CLT) and social distance theory of power, uses a behavioral investigation experiment to examine the role of PD in WTE facilities. The findings reveal that shorter PD is associated with higher public perception of risk and lower perception of economic benefits, fairness, and public acceptance. Additionally, a closer social distance of power tends to correspond with lower construal levels and a greater inclination to support the construction of WTE facilities, further supporting the CLT and social distance theory of power. The findings provide theoretical support and practical guidance for the sustainable development of WTE facilities, encouraging a sense of shared destiny and collaborative governance across society.

Research on resident participation in neighborhood regeneration provides valuable insights for urban policymakers in environmental governance. While previous studies have extensively examined various influencing factors, they often neglect the impact of behavioral inertia. To address this gap, this study conducts a behavioral experiment to quantitatively assess the presence and impact of behavioral inertia on residents' governance and financial participation behaviors. A total of 576 valid survey questionnaires were collected, and conditional logit model and ordered logit model were utilized for analysis. The study reveals that behavioral inertia is indeed observable in residents' governance participation and financial participation behaviors. Furthermore, the findings underscore distinct drivers of behavioral inertia for these two types of participation behaviors, with emotional reactions predominantly influencing governance participation, while short-term thinking largely shapes financial participation. Theoretically, this study uses the innovative concept of “behavioral inertia” to offer a new explanatory framework for aspects of behavior that cannot be solely explained by the attributes of regeneration plans. Furthermore, the behavioral experiments utilized in this study exemplify how the research framework of behavioral science can be applied to the study of urban governance in a broad context internationally. Practically, the research findings provide valuable insights for urban policymakers to tailor measures aimed at promoting resident participation and fostering sustainable urban development.

International trade in energy and environmental goods (EEGs) promotes the diffusion of green technologies and provides environmental benefits to trading countries. However, the uneven distribution of environmental benefits has hindered international negotiations on trade liberalization. This study investigates whether the disparity in technological content of EEGs leads to uneven emission reduction effects in the global value chain (GVC). Based on data panel covering 246 EEGs and 103 countries from 2001 to 2019, a dynamic threshold model was applied to examine the uneven emission reduction effects from the EEG trade. The results revealed that (1) the technological content of EEGs differs among countries, but this difference does not directly contribute to an uneven distribution of environmental benefits.(2) the technological content of EEGs impacts emissions through a threshold effect. Trading high-tech EEGs benefits from economies of scale, whereas trading low-tech EEGs suffers from diseconomies of scale. (3) The proportion of countries benefiting from trade in EEGs is increasing, with imports providing broader benefits than exports. These findings elucidate the situation and mechanism of the uneven distribution of environmental benefits in the EEG trade and offer insights into policy focus shifts.

Ecological compensation is a crucial measure to balance the interests of all stakeholders and mobilize local government's initiative in pollution control, while the relationship between ecological compensation and environmental quality standards may influence the policy effect. This study investigates the impact of China's first ecological compensation policy for air pollution control—Shandong Province's Ambient Air Quality Ecological Compensation (AAQEC) policy—on ambient air quality, and explores the appropriate relationship between ecological compensation and environmental quality standards. The theoretical model suggests that the lenient punishment for substandard environmental quality deviates the incentives of the AAQEC that targets multiple pollutants, thereby limiting its effectiveness in improving overall regional ambient air quality. Utilizing monthly air pollutant concentration data and employing a continuous difference-in-difference approach, the empirical results show that the AAQEC acts as a guaranteed measure to assist local governments in achieving environmental quality compliance. The AAQEC effectively reduces SO₂ and PM_2.5 concentrations through incentivizing environmental protection expenditures and green transformation of development mode, while the effect on controlling the key pollutant with severe exceedance is not observed—the concentration of PM₁₀ does not change significantly. The above plight is attributable to local government's strategic behaviors of selecting pollutants with higher marginal net benefit of pollutant abatement for priority treatment regardless of actual pollution conditions, resulting in the partial improvement in ambient air quality. These findings indicate that a flexible ecological compensation policy should be established with punishment imposed for substandard environmental quality, playing its role in enhancing local governments' environmental governance capacity and further improving overall ambient air quality based on compliance.