Impact Assessment Model Research Articles

Fast optimization of the emission reduction pathways of major air pollutants in China: From the perspective of different decision preferences.

To address the concern of optimization problem of China's PM2.5 control and the limitation of computational efficiencies for traditional air quality models, we developed an integrated analysis framework to efficiently establish the identification and cost-benefit assessment of PM2.5 control pathways in China by constructing a rapid PM2.5 exposure response method based on the high-order decoupled direct method (HDDM) and coupling the sequential least square algorithm (SLSQP) and health impact assessment model. Six emission reduction scenarios with varying decision preferences were analyzed. Our study provides a methodological approach for the rapid optimization of emission pathways of major air pollutants in China with flexible options in terms of objectives and constraints, fully considering the diverse differences in environmental, health, and economic impacts among different pollution sources simultaneously. Our findings based on the multi-scenario analysis strengthen the understanding of the importance of diverse species and regions for emission reduction among various decision preferences in China, confirm the necessity of implementing differentiated control strategies for distinct pollution sources from both cost and cost-effectiveness perspectives, and indicate that accelerating PM2.5 control process in the early stage is a beneficial choice for achieving more health benefits.

Anaerobic Co-Digestion of Food Waste and Groundnut Shells: Synergistic Impact Assessment and Kinetic Modeling

Anaerobic Co-Digestion of Food Waste and Groundnut Shells: Synergistic Impact Assessment and Kinetic Modeling

ML tools and systems models for WEFE nexus policy formulation and impact assessment

ML tools and systems models for WEFE nexus policy formulation and impact assessment

Revealing sources for synergistic control of PM2.5, O3, and CO2 in China: Based on social costs of air pollution and climate impact.

China is concurrently facing the dual challenges of air pollution and climate change. Here, we established a coupled modeling framework that integrated a chemical transport model with a health impact assessment model and the human capital method, to quantify the contributions of 150 emission sources (five sectors in 30 provinces) to the CO2 emissions, and the mortality burdens attributed to O3 and PM2.5. We found that, in 2019, the estimated premature deaths in China attributed to PM2.5 and O3 pollution were 1,499,073 and 143,420, respectively. The social cost of air pollution was approximately 232 billion USD (PM2.5: 212 billion USD, O3: 20 billion USD), comparable to the social cost of CO2 emissions at 246 billion USD. The social costs of air pollution and carbon emissions attributable to the 150 emission sources exhibited significant heterogeneity. We identified the control priorities and primary control targets for each emission source. Consequently, based on the social costs of air pollution and climate impact, we proposed a synergistic emission control policy that accounted for spatial distribution and sectoral categories. This policy aimed to harmonize the control strategies for PM2.5 pollution, O3 pollution, and CO2 emissions, thereby enhancing the comprehensive benefits of mitigation measures. Our study sheds light on optimizing emission control policies, enhancing the realism of relevant policy-making for synergistic control of air pollution and carbon emissions.

Remote sensing research on plastics in marine and inland water: Development, opportunities and challenge.

The accumulation of plastic waste from various sources into marine and inland water is considered a global problem due to its serious impacts on aquatic ecosystems and human health. In the past decade, remote sensing has played an important role in monitoring of plastic pollution in marine and inland water sources and has achieved a series of research results in this field. In this study, a comprehensive review was conducted on the development, opportunities, and challenges of datasets and methods in Marine and Inland Water Plastics Remote Sensing (MIWPRS) monitoring over the past decade, based on the Web of Science (WOS) core database. The results indicated that compared with traditional methods, remote sensing has attracted the attention of scholars due to its advantages. Since 2014, the number of related publications has been increasing year by year, especially in China and the United States, which have achieved tremendous development. The MIWPRS research focus mostly on the use of different satellite remote sensing data and related algorithms to obtain the distribution of plastics in marine and inland water. However, it faces the challenge of lacking subsequent systematic impact assessment models and key pollution prevention measures. In terms of data acquisition, there is a lack of continuous observation models due to the fluidity of marine and inland water. Therefore, MIWPRS has great development opportunities in developing specialized sensors and combining multi-source data with interdisciplinary knowledge such as artificial intelligence (AI) and GIS. It is necessary for us to improve the seasonal migration model of plastics in water and promote the development of MIWPRS towards broader and deeper fields.

Socio-Economic Assessment of Wind and Tidal Energy in Maritime Regions: Advancing SDG 7 Through Renewable Energy Development

This study evaluates the comparative socio-economic impacts of wind and tidal energy in maritime regions, specifically examining their contribution to achieving SDG 7 (Affordable and Clean Energy) through job creation, economic benefits, and sustainable energy production. The research employs sustainable energy transition theories and socio-economic impact assessment models, incorporating frameworks for renewable energy development in coastal regions and SDG implementation strategies. A mixed-methods approach combines quantitative analysis of economic indicators (employment, revenue, tariffs) with qualitative assessment of stakeholder perspectives. Data collection included document analysis, stakeholder interviews, and comparative case studies of wind and tidal energy projects. Analysis reveals wind energy's superior immediate economic viability, generating 3.68 jobs/MW during construction with competitive tariffs (7-8 cents/kWh). Tidal energy demonstrates higher job intensity (9 jobs/MW) but faces higher operational costs. Both technologies show distinct environmental impacts and stakeholder acceptance patterns, contributing differently to SDG 7 targets through clean energy provision and economic development. The findings provide evidence-based guidance for policymakers implementing SDG 7 in maritime regions, offering insights into optimizing renewable energy development for both economic benefit and sustainable development goals. This research presents a novel comparative analysis integrating socio-economic impacts with SDG 7 objectives, providing practical insights for sustainable energy development in maritime regions.

Socio-Economic Assessment of Wind and Tidal Energy in Maritime Regions: Advancing SDG 7 Through Renewable Energy Development

This study evaluates the comparative socio-economic impacts of wind and tidal energy in maritime regions, specifically examining their contribution to achieving SDG 7 (Affordable and Clean Energy) through job creation, economic benefits, and sustainable energy production. The research employs sustainable energy transition theories and socio-economic impact assessment models, incorporating frameworks for renewable energy development in coastal regions and SDG implementation strategies. A mixed-methods approach combines quantitative analysis of economic indicators (employment, revenue, tariffs) with qualitative assessment of stakeholder perspectives. Data collection included document analysis, stakeholder interviews, and comparative case studies of wind and tidal energy projects. Analysis reveals wind energy's superior immediate economic viability, generating 3.68 jobs/MW during construction with competitive tariffs (7-8 cents/kWh). Tidal energy demonstrates higher job intensity (9 jobs/MW) but faces higher operational costs. Both technologies show distinct environmental impacts and stakeholder acceptance patterns, contributing differently to SDG 7 targets through clean energy provision and economic development. The findings provide evidence-based guidance for policymakers implementing SDG 7 in maritime regions, offering insights into optimizing renewable energy development for both economic benefit and sustainable development goals. This research presents a novel comparative analysis integrating socio-economic impacts with SDG 7 objectives, providing practical insights for sustainable energy development in maritime regions.

Maximising the value of research projects for sustainable society development: The triple S system of social impact assessment – A case study of Chulalongkorn University research projects, Thailand

This study develops an SDG-aligned social impact assessment (SIA) system for five Chulalongkorn University research projects, in Thailand. Employing mixed methods in an exploratory sequential design, the first phase involves qualitative data collection, including focus groups and field studies, to establish the SIA system. The second phase employs quantitative data collection and applies SIA and social return on investment (SROI) approaches to evaluate these projects. The study yields the ‘triple S system’ (3S), comprising three components: strategic SDGs (S1); social impact assessment models (S2) encompassing goal-based, expert, and development evaluations; and SIA steps (S3), including the six-step ‘3I3S process’. These steps involve SDG indicator mapping (I1), impact pathway analysis (I2), identification of output, outcome, and impact indicators (I3), site visits and focus groups (S1), SROI analysis (S2), and summarisation of SDG social impact assessment results (S3). The study reveals alignment with specific SDGs (2, 4, 5, 8, 11, and 17) across the five projects. Projected 5-year SROI assessments range from 1.25 to 9.82. This dynamic system effectively monitors project progress toward social impact while identifying opportunities for enhancing the SIA process. This study contributes an SDG-aligned SIA system to enhance research project impact assessment, fostering more effective, sustainable, and socially beneficial initiatives.

Incorporating stressor interactions into spatially explicit cumulative impact assessments.

Human-induced stressors are impacting the oceans and reducing the biodiversity of marine ecosystems. The many stressors affecting marine environments do not act in isolation. However, their cumulative impact is difficult to predict. Most of the available methods for quantifying cumulative impacts on marine ecosystems sum the impact of individual stressors to estimate cumulative impact. We demonstrate how experimental evidence from interacting stressors can be accounted for in cumulative impact assessments. We adapted a widely used additive model to incorporate nonadditive stressor interactions into a marine spatially explicit cumulative impact assessment for seagrasses. We combined experimental data on the impact of multiple stressors with spatial data on stressor intensity to test whether stressor interactions impact seagrasses in a case study region in South Australia. We also assessed how uncertainty about cumulative impacts changes when uncertainty in stressor interactions is included in the impact mapping. The results from an additive spatial cumulative impact assessment model were compared with results from the model incorporating interactions. Cumulative effects from the interaction model were more variable than those produced by the additive model. Five of the 15 stressor interactions that we tested produced impacts that significantly deviated from those predicted by an additive model. Areas of our study region that showed the largest discrepancies between the additive and interactive outputs were also associated with higher uncertainty. Our study demonstrates that the inclusion of stressor interactions changes the pattern and intensity of modeled spatial cumulative impact. Additive models have the potential to misrepresent cumulative impact intensity and do not provide the opportunity for targeted mitigation measures when managing the interactive effects of stressors. Appropriate inclusion of interacting stressor data may have implications for the identification of key stressors and the subsequent spatial planning and management of marine ecosystems and biodiversity.

Sandia National Laboratories Impacts

Technology transfer (T2) from government laboratories to private sector partners is an activity encouraged by the federal government through policy mandates. This study evaluates the economic outcomes of T2 contracts between Sandia National Laboratories and outside partners between 2000 and 2010 to quantify the economic contribution of these agreements to the U. S. economy and identify their practical value to the federal government. The research team surveyed T2 partners by asking questions regarding sales of new products and services resulting from the T2 agreements. They asked additional questions of companies whose agreements focused on nuclear weapons, stockpiles, and non-proliferation. The team obtained full or partial information from 93% of the Cooperative Research and Development agreements and license agreements. The IMPLAN economic impact assessment model was used to estimate the economic impact of the related technologies. Major findings from the study included $53.7 billion in total sales of new products and services from these agreements, including $21.9 billion in sales of products to the government. The total economic impact nationwide was $95.9 billion. Other findings included $9.9 billion in new tax revenues, nearly $1.7 billion in estimated cost savings, and 434,464 jobs supported, with an average compensation of approximately $69,000 per year.

Diet Impact Assessment model: an interactive tool for analysing the health, environmental and affordability implications of diets

Abstract Global diets, environmental factors, and health require a holistic approach in public health discourse that takes into account the well-being of both humans and planetary health. Modeling tools have demonstrated being useful to estimate priorities and differences between countries and gain insight into future paths that depend on predetermined priorities. Modeling efforts that include environmental goals indicate the possibility of reducing environmental impacts while both improving food quality and overall health outcomes, especially in high-income settings. The aforementioned forecasts highlight the effectiveness of giving priority to dietary recommendations that lead to better, balanced patterns as the most effective approach to reducing mortality risks and environmental implications. The presentation will introduce the Diet Impact Assessment (DIA) model - a new interactive modelling tool for analysing the health, environmental and affordability implications of diets and dietary change. The tool enables countries to analyse user-specific scenarios of dietary change, and to estimate the health, environmental and cost burden of each scenario in terms of diet costs, avoidable deaths, changes in resource use and compatibility with global environmental targets, including those associated with food-related greenhouse gas emissions, land use, water use and fertilizer application. The tool was commissioned by the WHO Regional Office for Europe and is based on analytical frameworks developed by Marco Springmann and colleagues.

Mind the gap: from real-world policies to health impact assessment modelling

Abstract When conducting health impact assessments (HIA), researchers often face the challenges of trying to align as closely as possible with policy scenarios. This phase concerns the translation of real-world policies into estimates usable in models. Most of the time, researchers need to address a policy-impact gap between what is needed by the policymakers and what is believed to be reliable data fitted for the model. These mismatches can stem from differences in how exposure and outcomes are studied compared to the policy plan. By looking at a case study of the planned Belgian policy to reduce tobacco selling points, challenges during the pre-modelling phase were identified. The question of what an effect size should be and its importance in the HIA process were considered fundamental for the analysis. In particular, literature on exposure to tobacco sales points differed in outcomes, as it would target proximity to someone’s home, while the policy itself focuses on the reduction of points of sale of a specific store type. Similarly, smoking behaviour can be measured in various ways, such as initiation, cessation, relapse rates, or overall smoking prevalence. The policies’ effect on these various outcomes differed significantly. In this presentation, the discussion will focus on the alignment of the existing literature with the specific details outlined in the policy. We will explore the challenges that arose when existing research criteria and assumptions did not fully match the policy, taking the new Belgian tobacco plan as an example. Lastly, we will highlight the opportunity to set up monitoring systems to evaluate tobacco interventions to extract reliable estimates for HIA and strengthen evidence-based tobacco policies in the future.

A Study on Illegal Wildlife Trade in Tanzania Based on Multi-source Data Fusion

This study on the illegal wildlife trade crisis in Tanzania is methodically divided into three phases: planning, policy, and application. Initially, data preprocessing methods such as null processing, interpolation, and max-min normalization were applied to construct a client impact assessment model utilizing AHP and hierarchical evaluation methods. WWF emerged as the most influential client. The policy phase introduced a comprehensive five-year strategy across economic, social, cultural, and environmental sectors. This strategy was supported by three quantitative models, which were used to evaluate the impact of the illegal wildlife trade. The economic model assessed the contribution to GDP growth from the integration of illegal trappers into the workforce. The social model applied crime economics to optimize time allocation under uncertainty. The cultural impact was measured through educational guidelines and a literacy score. Finally, environmental policies were analyzed by adjusting nature reserve areas and poaching difficulty parameter Z. In the application phase, Tanzania's National Forest Park was selected to serve as a pilot site for the models, with the projected outcomes including an increase in species richness and a reduction in poaching ease over a five-year period.

Examining global biodiversity accounts: Implications of aggregating characterization factors from elementary flows in multi-regional input-output analysis.

Extending multi-regional input-output (MRIO) models with spatially explicit life cycle impact assessment (LCIA) models allows practitioners to quantify biodiversity impacts at every step of global supply chains. Inconsistencies may be introduced, however, when high-resolution characterization factors (CFs) are aggregated so as to match the low spatial granularity of MRIO models. These aggregation errors are greater when CFs are aggregated via proxies, such as ecoregion land shares, instead of based on spatially explicit elementary stressor flows. Here, we describe our approach to tailoring application-specific CFs for use in MRIO studies. We apply a global agricultural production model, Spatial Production Allocation Model (MapSPAM), with the LCIA database, LC-IMPACT, to create crop-specific national CFs. We investigated i) if the differing aggregation approaches and the increased spatial explicitness of the constructed CFs deviate substantially from those in LC-IMPACT, and ii) what the resulting consequences for national production and consumption-based biodiversity footprints are when combining the tailor-made CFs with the EXIOBASE MRIO model. For the year 2020, we observe an increase in global production-based biodiversity impacts of 23.5% for land use when employing crop-specific CFs.

Machine learning-based surrogate models for fast impact assessment of a new building on urban local microclimate at design stage

The rapid urbanization introduces changes in the local urban microclimate. Many efforts have been paid on the impact assessment of building design on local microclimate. However, there is still a lack of efficient and accurate prediction method for assessing the impacts on local microclimate when making the design of individual buildings. Two complementary machine learning-based surrogate models are proposed, including an SVR-based local air temperature model and a LightGBM-based local wind velocity model. They are identified by evaluating and comparing eight alternative machine learning models, four for each model development. 200 sets of CFD simulation data corresponding to different building designs are used for the model training and validation. The results show that the developed surrogate models can dramatically reduce computation time (from over 5 h to less than a second for a single prediction) while keeping the same order of accuracy of CFD simulations for local microclimate prediction of individual buildings. It therefore facilitates the fast, comprehensive and accurate prediction of the impacts on the local microclimate at the early design stage of new construction and renovation of individual buildings, for designers to deliver preferred local microclimate and/or avoid unacceptable microclimate changes.

Calibration and validation of a national transport computable general equilibrium model for economic impact assessment

Infrastructure investments are being undertaken to improve efficiency, economic activity, and growth. Therefore, policy makers are increasingly looking for modelling tools that can evaluate the impact of transport infrastructure on the economy, firms and households from the lens of economic activity and equity. We have developed a large scale national CGE model for India that is calibrated to economic data as well as validated to data observed from the Indian Railways. The novelty of this work lies in three main areas (1) The CGE model incorporates multi-modal transport with road and rail (2) It is the largest model developed so far with 33 regions, 29 industries, 312,530 kms of highways and 126,366 km of Railways (3) Validation of the CGE model. The CGE model was able to predict the tonnage freighted on the Western Dedicated Freight Corridor to within 3.9 % accuracy. The Dedicated Freight Corridor Network (DFC) contributes a 2.94 % increase in the revenue for the railways as well as a 0.08 % increase in GDP. This implies that DFC can directly contribute INR 160 billion to the 2019 GDP. The CGE model distributed the benefits of faster and cheaper alternatives to the entire economy. This was observed during the evaluation of the road projects that were found to improve GDP, household welfare and rail revenue for rail, as well as improved equity.

The impact of energy sector pollution on human development and inequality amidst climate change

Abstract The provision of energy is a primary contributor to climate change and environmental pollution, the latter including air pollution from aerosol emissions and a broad range of other human health effects and ecosystem damages. At the same time, aerosol emissions also reduce radiative forcing, leading to a masking of part of the warming due to rising CO$_2$ concentrations and temporarily reducing the adverse impacts of climate change. Using an energy-economy-climate model linked to atmospheric chemistry, health impact and life cycle assessment models, we show that the societal costs of the health impacts of environmental pollution substantially exceed the economic benefits from masked warming. We further highlight the unequal burden of these issues on developing countries and explore the implications for human development, global inequality and international climate policy.

Reasoning in Institutional Discourse – The Development of a Sustainable Impact Assessment Model

Reasoning in Institutional Discourse – The Development of a Sustainable Impact Assessment Model

Biodiversity on the line: life cycle impact assessment of power lines on birds and mammals in Norway

The global shift towards renewable energy plays an important role in fighting climate change. To facilitate the global growth of renewable energy production, the expansion of the electric grid becomes inevitable. Yet further construction of power lines poses a risk to biodiversity. Power lines traverse natural habitats and can lead to habitat conversion, fragmentation, and loss. Moreover, due to collisions and electrocutions, power lines kill hundreds of millions of birds each year. These impacts, however, have so far not been incorporated into decision-making. Life cycle assessment (LCA) is a widely used framework to compare environmental impacts and support decision-makers in planning and promoting sustainable strategies. We adapted existing life cycle impact assessment (LCIA) models to quantify the three main impacts of power lines on biodiversity: collision, electrocution, and habitat conversion and fragmentation. Our models incorporated species-area and species-habitat relationships to assess the effects of power lines on the diversity of birds and non-flying mammals in Norway, as the country is currently committed to reducing its emissions by expanding its renewable energy capacity. Overall, habitat conversion and fragmentation had the highest impact across the three impact categories, particularly affecting mammal richness. Furthermore, distribution lines often affected species richness more than transmission lines. The effect of the three impact pathways varied among different species groups, highlighting the vulnerability of certain species to habitat change, collision, and electrocution. Integrating LCIA models that quantify the impacts of power lines on biodiversity into LCA can support decision-makers with tools to promote the development of the electric grid without overlooking its effect on species richness. In addition, our models set the stage for a comprehensive assessment of the effects of electricity generation and transmission on biodiversity.

INTEGRATED ASSESSMENT OF CLIMATE CHANGE ADAPTATIONS FOR THE RESILIENT AGRICULTURAL PRODUCTION SYSTEM IN SOUTH PUNJAB, PAKISTAN

Agricultural production systems are multidimensional, interlinked, complex and diverse, and need special attention to cope with changing climate, technology and changes in input and output market structure. Climate change is an evident phenomenon that drastically affects the agricultural production system. The developing countries mainly rely on farming activities for the needs of food and Fiber. This study aims to find climate change impacts and economic analysis of proposed adaptations and to formulate a climate-resilient agricultural production system. The primary and secondary data were collected by conducting a well-designed South Punjab survey. The secondary data about historical crop yields, socioeconomic profiles, climate data, and crop simulation results are incorporated into the model. The TOA-MD (Tradeoff analysis multidimensional impact assessment) model was used for the economic analysis. The results showed substantial negative impacts of climate change on farm income. The adaptations regarding climatic variations are present and increase the net farm returns adopted by farming communities, which are projected in public policies. The financial inclusion of the farming community is needed in terms of farm insurance and finance.