Current watershed planning in many developing countries primarily focuses on biophysical features, often neglecting socioeconomic and behavioral factors. This study integrates biophysical variables with socioeconomic characteristics and behavioral traits, such as individualism, trust in government organizations, greed, environmental preferences, and perceived financial ability to develop a more comprehensive and sustainable approach to watershed management. The Ganj basin in Iran, consisting of three villages, was selected as the study area. Socioeconomic data from the traditional sanctums of each village were collected through surveys and interviews, and Spearman's rank correlation coefficient was used to analyze the relationship between participation history and socioeconomic traits. These attributes were then mapped and overlaid with biophysical layers to guide planning decisions. Findings reveal that while traditional rural sanctums share similar socioeconomic conditions, they exhibit significant differences in behavioral tendencies, particularly in individualism, greed, and financial perception. The Ganj sanctum demonstrated the highest preference for participation in watershed plans. Additionally, the study highlights that relying solely on existing biophysical and socioeconomic government data is inadequate for comprehensive and participatory watershed management. Instead, local assessments, such as questionnaire-based studies, can uncover critical insights into both tangible and intangible factors, thereby improving planning accuracy. These findings provide valuable guidance for policymakers in designing management strategies tailored to the socioeconomic and behavioral characteristics of each sanctum, ensuring that watershed plans are more sustainable and long-lasting.

Watersheds in Canada are being negatively affected by cumulative effects from anthropogenic activities. Canada has difficulty integrating cumulative effects into decision-making processes for both individual development projects and entire regions. The Canadian Watershed Research Consortium (CWRC) was a research program created by Canadian Water Network (CWN) that started in 2010 and formally ended in 2015. The purpose of the program was to develop cumulative effects assessment monitoring frameworks for six different watersheds across Canada. In this paper, we evaluated the impact of the CWRC on the capacity for decision-making in these watersheds using an adapted Research Contribution Framework as a methodological guideline. Data were collected through 14 semi-structured interviews and a document analysis of eight primary CWRC reports along with secondary information (57 journal publications). From the interviews, primary documents, and a scoring exercise, we found that the CWRC was successful in contributing to research uptake and research use, but that it may have fallen short in its overall research impact. We determined that some nodes (Muskoka, Northumberland Strait, Slave River) were able to institutionalize their cumulative effects monitoring frameworks in that an organization continues to collect data that will be used to inform policy and management. Factors contributing to research contribution included strong leadership and interest from governments and communities for maintaining at least some aspects of the monitoring program beyond the end of the Consortium. Important considerations for future research in this area include long-term follow-up and the relationship between cumulative effects monitoring and decisions made through environment assessments and watershed planning.

Carbon capture and storage (CCS) is a technology implemented to reduce emissions in the power and industrial sectors by capturing carbon dioxide (CO2) before it is released into the atmosphere. Emerging technologies like CCS are often unfamiliar to the public and can be misinterpreted when publics are not involved in the decision-making process, leading to opposition and barriers to deployment. Engaging the public on policy decisions can prevent such obstacles from hampering implementation. This study aims to elicit preferences and estimate the distribution of willingness to pay (WTP) for carbon capture and storage in the United Kingdom (UK). We employ the contingent valuation method with double bounded dichotomous choice format, administering an online survey to 1033 individuals from the UK. Interval regression analysis is applied to estimate mean WTP. Our findings indicate that public attitudes towards CCS in the UK tend to be relatively positive. Mean WTP for the implementation of CCS is £95.50. We find that environmental attitudes more than socio-demographic characteristics are significant factors in WTP decisions for CCS. Public acceptance is critical to ensure appropriate steps are taken to move CCS projects and policy forward and prevent further delay in tackling emissions in the energy sector.

Environmental Impact Assessments (EIA) are key to infrastructure development globally, and serve to evaluate impacts and guide approval. Importantly, the EIAs typically include mitigation measures through which projects - if allowed to advance - will reduce and mitigate the most negative social and environmental impacts. There are mounting demands to ensure EIA assessments and procedures meaningfully address these impacts, particularly for large-scale, high-risk and long-lived projects such as hydropower infrastructure. We explore these challenges in Nepal, where more than 450 EIAs have been approved since 1997. However, existing EIA evaluations and procedures are widely recognized as failing to deliver on-the-ground. We provide the first public dataset of hydropower projects with approved EIA's, as a basis for characterizing the state of EIA implementation in Nepal. Based on a purposive sample of hydropower EIA reports, we highlight 26 commonly-proposed mitigation measures, and examine the thoroughness of these reports as written. We then also evaluate whether and how these proposed mitigation measures were implemented, based on independent, field-based evaluations of 9 projects, including interviews with field staff and local residents. The results show strong patterns of incomplete and non-compliance EIA reports, particularly as relates to mitigation measures. In the field, we document low levels of implementation of proposed mitigation measures, as well as blatant violations of best practices, and very low rates of government monitoring (~15% of approved projects). We leverage these insights to propose practical advice to strengthen EIAs implementation in Nepal, with broader implications for assessing and mitigating long-lived, high-impact infrastructure projects.

This study explores the impact of NIMBY (Not In My Backyard) sentiments on local resistance to wind power developments in Norway. With a reopening of concession processing for new onshore wind power projects in 2022, a significant number of applications await evaluation, provoking substantial local opposition. This research assesses the prevalence and impact of NIMBY attitudes among the Norwegian public and to critically examine the theoretical validity and practical utility of the NIMBY concept. Using data from an online survey, which yielded 1220 complete responses, we analyzed general attitudes toward wind power in Norway and specific attitudes toward local wind power installations. While 37% of respondents support wind power construction in Norway, only 27% favor it near their homes. Based on the relationship between these two attitudes, we identified the proportion of NIMBYs in the data using two approaches. A strict definition requires individuals to support wind power in Norway but oppose its presence in the natural areas near their own homes. A less strict definition also includes those who expressed a neutral stance toward wind power in Norway among the NIMBYs. In both cases, a relatively small segment of respondents exhibit classic NIMBY characteristics, i.e., support (or claim neutrality) to wind power in general but opposing it locally. Further analysis reveals that direct experience with wind power installations is associated with increased acceptance rather than opposition, challenging the NIMBY narrative. Our study argues that labeling local resistance as NIMBYism oversimplifies the issue and ignores other significant factors like environmental identity, place attachment, and broader environmental attitudes. Hence, the study suggests that blaming wind power opponents as “Nimbys” often is misplaced and unjust.

Prescribed burning is a key tool in land management globally used to reduce wildfire risks and achieve ecological, cultural and resource management objectives across both natural and human systems. Despite its widespread application, research on prescribed burning is marked by significant gaps. Subsequently, we posed the following research questions: (1) What are the key research topics that define international, peer-reviewed literature on prescribed burning? (2) What are the temporal and spatial trends of these topics? (3) What are the relationships between the national income of a given country and the trends in research topics? And, (4) What are the most salient knowledge gaps in peer-reviewed prescribed burning research, and how can they be addressed? We used structural topic modelling and geoparsing to conduct a detailed text analysis of 7878 peer-reviewed articles on prescribed burning. We revealed that research on prescribed burning is dominated by studies from high-income countries, particularly the United States. This highlights a geographical bias that may skew global understanding and application of prescribed burning practices. Our topic modelling revealed the most prevalent topics to be Fire Regimes and Landscape Biodiversity Management, whilst topics such as Air Pollution & Health, and Wildfire Risk Management gained prominence in recent years. Our analysis highlighted a disconnect between forestry-related research and broader landscape management topics. This finding emphasises the need for more interdisciplinary research, and research on the use and effects of prescribed burning in diverse ecosystems and underrepresented regions, particularly in the context of climate change.

Cyanobacterial blooms in shallow lakes pose a significant threat to aquatic ecosystems and public health worldwide, highlighting the urgent need for advanced predictive methodologies. As impounded lakes along the Eastern Route of the South-to-North Water Diversion Project, Lakes Hongze and Luoma play a key role in water resource management, making the prediction of cyanobacterial blooms in these lakes particularly important. To address this, satellite remote sensing data were utilized to analyze the spatiotemporal dynamics of cyanobacterial blooms in these lakes. Subsequently, a precise machine learning model, integrating the Projection Pursuit Model and Random Forest (PP-RF) algorithms, was developed to predict the extent of cyanobacterial blooms, considering a range of influencing factors, including physical, chemical, climatic, and hydrologic variables. The findings indicated pronounced seasonal fluctuations in cyanobacterial blooms, with higher levels in summer than in other seasons. Key determinants for cyanobacterial blooms prediction included solar radiation, temperature and total nitrogen for Lake Hongze, while for Lake Luoma, significant predictors were identified as temperature, water temperature, and solar radiation. Compared with traditional data preprocessing methods, PP-RF model has advantages in addressing multicollinearity. This study provides a feasible method for predicting cyanobacterial blooms in impounded lakes within inter-basin water transfer projects. By inputting region-specific data, this model could be applied broadly, contributing to against the adverse effects of cyanobacterial blooms and provide scientific guidance for the protection and management of aquatic ecosystems.

Most Environmental Impact Assessments (EIAs) fail to generate effective monitoring and forecast triggers because there is a lack of appropriate baseline data and forecasting, especially for biotic endpoints. Herein, we provide an example of how to develop monitoring and forecast triggers with biotic data, specifically fish populations, to assess impacts of a planned refurbishment of the Mactaquac Hydroelectric Generating Station, a large hydroelectric facility. We recommend strategies for developing interim monitoring triggers until sufficient biological data is collected, including default critical effect sizes or data percentiles when there are only a few years of data. When there is sufficient data the monitoring trigger can be based on the predicted normal range, i.e., 2x standard deviation of the means. We generated forecast triggers with the general linear model, partial least squares regression, and elastic net regression. We demonstrate that interannual variability of fish population characteristics sampled consecutively for 4 years was insufficient for meaningful monitoring and forecast trigger development. Collecting sufficient baseline data for new projects in an undeveloped area will be challenging due to costs and regulatory and economic time frames as current practice is generally 1 or 2 years. Changes to existing projects, such as in this study, or new projects near existing development should have existing baseline data – if forethought is given as to effective endpoints. The alignment of monitoring requirements between developments within a watershed will improve monitoring, modelling, and prediction over the long term and for consideration of future developments.

Cumulative effects result incrementally from a series of actions over time. On their own, the causative actions behind cumulative effects may be one-off and minor but become significant when they aggregate. Drawing on a practice example from Aotearoa New Zealand—the widespread expansion of irrigation over thirty years and associated land use changes—this paper extends our understanding of cumulative effects in several ways. First, the example shows that in Aotearoa New Zealand, the institutional and practice aspects of cumulative effects assessment are poorly developed, despite being required in legislation and recognized in case law. Part of the problem, we argue, is an unclear mandate for strategic environmental assessment that has limited the development of consistent cumulative effects assessment theory and practice. The “practice” element to improve, we suggest, is an integrated assessment of accumulating impact pathways, with particular implications for the conduct of social impact assessments. The key “institutional” improvement needed is a stronger role for strategic environmental assessment that incorporates social impact assessment, to address the social impacts of cumulative effects in advance, as well as for monitoring actual effects.