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. 2024 Dec 9;14(12):e083214. doi: 10.1136/bmjopen-2023-083214

Emission reduction strategies and health: a systematic review on the tools and methods to assess co-benefits

Sammila Andrade Abdala 1,2,✉, Kenza Khomsi 3, Anass Houdou 1,2, Ihssane El Marouani 1,2, Imad El Badisy 1,4, Houda Najmi 3, Majdouline Obtel 5, Lahcen Belyamani 1,6, Azeddine Ibrahimi 6, Mohamed Khalis 1,2,7
PMCID: PMC11628954  PMID: 39653556

Abstract

Abstract

Objective

The objective of this study is to review the current literature on the health co-benefits of emission reduction strategies and the methods and tools available to assess them.

Design

Systematic review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Data sources

PubMed, Scopus, Web of Science, ScienceDirect and GreenFILE were searched from January of 2017 to March of 2023.

Eligibility criteria

We included original, peer-reviewed journal articles that described emission (ambient air pollutant and greenhouse gases) reduction strategies and assessed their health co-benefits.

Data extraction and synthesis

Two independent reviewers employed standardised methods to search, screen and code the included studies, documenting their findings in an Excel spreadsheet.

Results

From 6687 articles, 82 were included. Most studies show that emissions reduction strategies improve air quality, reducing mortality and morbidity. Health risk assessment and health impact assessment are common, though procedures may cause confusion. About 33% used established models like the integrated exposure-response and global exposure mortality model. Out of all studies, 16% of them used Environmental Benefits Mapping and Analysis Program—Community Edition. Only 17.8% carried out cost–benefit analyses, but these show economic worth in investing in emission reduction strategies.

Conclusions

Emission reduction strategies significantly enhance human health, with potential co-benefits offsetting intervention costs, which can be an incentive for action in low and middle-income countries. This review emphasises investing in cost–benefit analyses and research, particularly in regions with limited studies on emission reduction and health co-benefits. It provides decision-makers insights into selecting assessment methods and underscores the ongoing need for model and tool evaluation.

PROSPERO registration number

CRD42022332480.

Keywords: Mortality, Systematic Review, PUBLIC HEALTH, Risk Factors, Climate Change

STRENGTHS AND LIMITATIONS OF THIS STUDY.

  • The study takes an innovative approach by simultaneously addressing air pollutant emissions and greenhouse gas emissions, contributing to a more holistic understanding of climate change and air quality challenges.

  • The study not only reviews the literature on emission reduction strategies but also assesses the methods and tools used in research. This dual focus enhances the completeness of the review, providing a comprehensive understanding of both the content and methodologies employed in the field.

  • The literature search was conducted across five databases, ensuring a comprehensive review, and all included studies were classified as high quality, enhancing the credibility of the results.

  • The included studies exhibit diverse characteristics in terms of design, population, interventions and outcomes, limiting the generalisability and complicating the interpretation of findings.

  • The study acknowledges the complexity of assessing health cobenefits, involving multiple disciplines and sectors. Challenges include uncertainties in modelling, data accessibility issues and the need for careful consideration of strategy implementation.

Introduction

There is currently plenty of evidence to say that climate change and air pollution have a strong impact on human health, directly and indirectly.1 2 The WHO3 states that air pollution (ambient and indoor) alone is responsible for seven million premature deaths a year, while climate change is expected to cause approximately 250 000 additional deaths per year between 2030 and 2050, from malnutrition, malaria, diarrhoea and heat stress. It also states that the direct damage costs to health are estimated to be between US$2 and 4 billion/year by 2030.4

Although it is known that carbon emissions and air pollutant emissions often originate from the same sources and that climate change affects air pollution and vice versa,5 6 historically, governmental actions to address climate change and air pollution have been largely separate.7 They fail to see that climate change policies can generate benefits to air quality, and consequently benefit the health of the population. Since the concern over the costs of mitigating climate change and reducing pollution is an important issue for decision-makers and may hinder them from taking action, it’s important to make them consider the benefits that investing in reducing emissions can bring to other important sectors, like health.

Many recent studies have shown that investing in climate change mitigation and air pollution reduction can bring co-benefits to health and the economy, which can be attractive to decision-makers.8,12 Mayrhofer and Gupta13 discuss how the concept of cobenefits is growing in use and how working with it can be very promising since it opens up a window of opportunity for climate policy goals to be achieved as a consequence of another goal that politicians consider to be more important. This study sees cobenefits as a ‘win–win’ strategy through which at least more than one objective is achieved through a single policy. According to the Intergovernmental Panel on Climate Change14 the term ‘co-benefits’, also known as ancillary benefits, is defined as ‘the positive effects that a policy or measure aimed at one objective might have on other objectives, thereby increasing the total benefits for society or the environment’.

Hence, it is imperative to consider the magnitude and impact of emission reduction strategies on health and their subsequent effects on the economy when making policy decisions. An increasing number of studies are assessing the health benefits of such strategies globally. Gao et al9 conducted a systematic review of 36 studies and found that reducing greenhouse gas emissions can result in significant and potentially cost-effective public health co-benefits. Similarly, Burns et al10 conducted a Cochrane systematic review that examined 42 studies on interventions aimed at reducing ambient air pollution and their impact on health. In addition to evaluating the interventions and their health co-benefits, the review also assessed the methods used to assess these co-benefits. The authors conclude that there is a pressing need for more rigorous study design, standardised evaluations and improved analysis methods to accurately measure the health impacts of these interventions. In a scoping review of various tools and methods to integrate health considerations into climate change adaptation and mitigation strategies and policies, Delpla et al15 also acknowledge that there is still a need for more research to analyse the different tools and their uncertainties and efficacy.

Overall, these reviews underscore the importance of understanding the potential health impacts of emission reduction strategies and the need for continued research and evaluation to improve the assessment of these interventions. However, to the best of our knowledge, there is no systematic review that discusses emission—greenhouse gases (GHG) and ambient air pollution—reduction strategies, health co-benefits and the tools and methods to assess them. The goal of this study is to review the current literature on emission reduction strategies and the methods and tools available to assess their health co-benefits.

Methods

Protocol and registration

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement16 and was registered and published in the International Prospective Register of Systematic Reviews (PROSPERO) on 6 March 2022 under the number CRD42022332480 and is available accessing the following link: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022332480.

Information sources and search strategy

A comprehensive literature search for peer-reviewed studies published from January 2017 to March 2023 was conducted using the following databases: PubMed (Medline), Web of Science, Scopus, ScienceDirect (Elsevier) and GreenFILE (EBSCO). The search strategy followed the Problem, Intervention, Outcome principle and was carried out using the following key themes: emission reduction, strategies, health co-benefits and effectiveness. The search terms were adapted to the different databases in order to facilitate a comprehensive search. The number of synonyms was reduced for some databases that limited the number of search words. The general and detailed search strategies are presented in the supplemental material (online supplemental tables S1 and S2).

Study eligibility criteria

Articles were eligible for inclusion in this systematic review if they were original, peer-reviewed journal articles that described emission (ambient air pollutant and GHG emissions) reduction strategies and assessed their health co-benefits. Articles published in English from January of 2017 to March of 2023 were included in order to capture the most recent developments and advancements in the field. Reviews, reports conference abstracts, books and meta-analyses were excluded. Articles that did not clearly specify and describe strategies, and the scenarios being compared, were also excluded. Articles that assessed only indoor air pollution interventions were also excluded, as well as studies that focused on population perceptions on health co-benefits of emission reduction. A significant number of articles were found that associated COVID-19 measures to a reduction in emissions, and consequently, an improvement in air quality and health. Even though this is an important topic, these articles were excluded, since the pandemic measures were not an intentional strategy targeted at reducing emissions. Another growing topic that appeared frequently in the studies is the importance of a healthy diet (green, plant-based diet) for emissions reduction and for health. However, the pathway to calculate the health impact was usually made directly from the intake of a vegetarian/plant-based diet and not going through the reduction of GHG/air pollutants and their impact on health. The same can be said of the intervention active transportation (TR), where the health benefit is calculated through the increase of physical activity and not emission reduction. Therefore, only the studies that measured health impact through the emission reduction pathway were kept in the systematic review.

Study selection

The search references were exported to the reference manager Zotero Reference Manager. After eliminating duplicates, two reviewers (SAA and KK) independently screened all titles and abstracts identified by the search for relevance to the review question. Any disagreements in the selection process were resolved through consultation with a third reviewer (MK). The full text of each article identified as potentially relevant by either one or more review authors was retrieved. The articles were then assessed by full text for eligibility according to inclusion criteria. If necessary, discrepancies or disagreements in this assessment were also resolved by consulting a third reviewer (MK). The eligible articles were identified using the PRISMA flow diagram.

Data extraction

The first author (SAA) extracted the data from the articles that were finally selected for inclusion in the review, and the second author (KK) checked decisions. Any discrepancies/disagreements to obtain or to confirm relevant data were resolved by consensus through discussion between the two authors, or with other authors (AH and MK) if necessary. An Excel spreadsheet was used for recording the extracted data, which included: authors and year; study region; involving sector(s); emission reduction strategies; benefits for climate and air quality; health cobenefits; methods and tools to assess health cobenefits; cost–benefit analysis.

Quality of reporting

The quality of reporting of the included studies was assessed using an adaptation of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) approach. As in previous systematic review,9 the adapted STROBE checklist was employed to create a binomial scoring system (criterion met=1, criterion not met=0). The score percentage of each article was based on the score of quality of reporting criteria met divided by the total number of criteria. To evaluate these scores, we categorised them into four groups based on percentage breakdown: excellent quality (≥85%), good quality (70% to <85%), fair quality (50% to<70%) and poor quality (<50%), following the thresholds used by Limaye et al.17 Assessments were performed by the first author (SAA) and checked by the second author (KK). Any disagreement was resolved by consensus or arbitration via a separate reviewer (MK). The full checklist is available on the supplemental material (online supplemental table S3).

Results

Study selection

A total of 6687 records were identified from the five different databases. After removing duplicates, reviews and books, 4439 articles were screened by title and abstract. Based on the inclusion criteria, articles that did not address emission reduction strategies and their quantitative health co-benefit were deleted. Out of 226 articles screened by full text for eligibility, 82 peer-reviewed articles were selected to be included in the analysis (figure 1).

Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart for screening process.

Figure 1

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Study characteristics

The number of published papers that address emission reduction strategies and health co-benefits is steadily growing (online supplemental figure S1), but this growth is not taking place homogeneously across the globe. Almost half of the studies (38) were conducted in China, followed by the USA (16). The other countries have three or less articles published in the last 5 years on this specific topic (figure 2). The majority of the studies adopted the approach of assessing the possible health co-benefits of implementing certain strategies in the near future (projected scenarios) (online supplemental figure S1). In all the years studied, the number of articles that used projected scenarios (76%) surpassed the studies that assessed implemented interventions.

Figure 2. Geographical distribution of the included studies.

Figure 2

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Emission reduction strategies and interventions

The different interventions assessed in the studies were categorised into five main sectors: transportation (TR, energy production (EP), industrial (IND), agriculture and food (AGR) and residential (RES). Energy consumption permeates almost all other sectors and processes and is difficult to separate; therefore, only EP was set apart as another sector of itself. The majority of the studies (66%) assessed interventions in the TR sector,18,69 followed by the EP sector (55%).1921 24 25 27 28 30,32 34 36 37 39 40 42 RES (35%),2122 24 27 28 31 32 34 36 37 39 40 42 43 45 49 52 55 56 59 64 67 71 74 90,94 IND (29%)21 24 25 27 28 34 36 37 39 40 42 43 52 55 56 64 67 73 82 95 96 and AGR (26%)21 24 25 27 28 31 34 36 37 39 40 42 43 52 55 56 67 92 93 97 98 had similar weight in the selected studies. In some studies, interventions target multiple sectors simultaneously, which is why the sum of sectoral percentages exceeds 100%.

In the TR sector, the articles addressed the following interventions: investing in public TR,41 electric vehicles,18 20 38 44 47 55 69 low-emission zones,50 65 66 carbon pricing,23 restrictions of private vehicles,67 retrofitting heavy-duty vehicles with particulate filters and oxidation catalysts,35 and the ban of sale of cars powered by fossil fuels.30 For EP, research was more focused in use of renewables,34 55 67 72 81 nuclear,72 decarbonisation of energy sector,30 55 72 coal regulation/banning,74 limits on carbon dioxide (CO2) emissions from new and existing fossil-fuel-fired electric generating units75 and control policies on coal-fired power plants.76 78 80 89 In the IND sector, the strategies that appeared were IND structure adjustment,73 energy efficiency,64 67 73 79 energy substitution,73 coal banning areas32 and decapacity policies.95 The RES sector focused on energy efficiency in housing,64 67 promoting clean fuels37 and reductions in RES solid fuel use (eg, replacement of coal-based stoves with stoves using liquefied petroleum gas),92 and firework regulations.94 Finally, the sector for AGR can be summarised in the strategies of prohibition on open biomass burning/zero-burning policy,36 97 and agricultural fertiliser emissions92 (online supplemental table S4).

Climate and air quality benefits

Fine particulate matter, 2.5 μm or less in diameter, (PM2.5) was by far the most used parameter to assess health cobenefits of emission reduction strategies, being mentioned in 93% of the studies (online supplemental figure S2). Around 45% of the studies address both air pollutants and at least one GHG. After PM2.5, the parameters most studied were nitrogen oxides (NOx) (40%), sulphur oxides (SOx) (33%), CO2 (29%) and ozone (O3) (21%). The majority of the studies presented climate and air quality benefits from the interventions/policies assessed, with the greatest benefits coming from implementing more stringent and joint strategies22 28 50 73 89 (online supplemental table S4). There are some exceptions, however, like in the study of Kwan et al,19 where the projected scenario of electric vehicle growth can generate an increase in levels of carbon monoxide (CO) and PM2.5. There were also other exceptions when studies addressed O3. Wang et al,36 for example, found that O3 concentration increased significantly as PM2.5 levels sharply decreased when interventions were implemented. Izquierdo et al49 found similar results, with decrease of PM2.5 and O3 increase. Lu et al68 observed an increase in the surface-ozone concentrations, as well as Lu et al.25 Despite this, Wang et al36 state that the number of avoidable deaths attributed to PM2.5 reduction is larger than the level of premature deaths related to increasing O3.

Health co-benefits

Almost all of the studies assessed the health co-benefits of emission reduction strategies using premature mortality as parameter (online supplemental table S4). There were also several studies that addressed cancer,32 93 asthma,5063 66 74,76 hospitalisations and emergency department visits for respiratory and cardiovascular disease each year,45 66 chronic obstructive pulmonary disease hospitalisations,25 birthweight,46 preterm births,63 75 autism spectrum disorder63 and term low birth weight,63 ischaemic heart disease,50 and fatal and non-fatal heart attacks.89 Several articles assessed health burden or disease burden as health outcome.25 36 71 82 The term burden of disease generally describes the total, cumulative consequences of a defined disease or a range of harmful diseases with respect to disabilities in a community.99 These consequences include health, social aspects and costs to society. It is often quantified in terms of quality-adjusted life years or disability-adjusted life years (DALY).99

Nearly all studies give evidence that emissions reduction strategies have the potential of improving air quality and human health, reducing mortality and morbidity (online supplemental file 5) (online supplemental table S4). As was seen in the last topic, however, there was an exception where a strategy of increasing the number of electric vehicles could increase PM2.5 and CO levels, and consequently, generate additional 719 respiratory deaths or 9900 DALYs per year from increased PM2.5 emissions and exposure.19 Nevertheless, the same study shows that respiratory mortality from NOx emissions and exposures could be avoided by up to 10 200 deaths or 176 200 DALYs per year in 2040. This could mean a reduction of 31 deaths per 100 000 population against an increase of 2 deaths per 100 000 population per year in 2040.19 Another exception is a study65 that assessed low emission zones in Germany, which reports that the emission reductions were too small to translate into substantial improvements in infant health. There was a case as well where the health impacts were lower than air quality benefits due to the implementation of the measures not affecting the most densely populated areas.73

Methods and tools for assessing health co-benefits

Several studies2021 23 27 29 39 42 50 52 53 59 61 62 64 66 69 74 75 83,85 91 94 mentioned using concentration–response (CR) functions and exposure–response functions (ERFs) directly without employing any known models for assessing health co-benefits (online supplemental table S4). Around 33% of the studies used established models, such as the Integrated Exposure-Response (IER) model,1828 35,37 56 71 78 the Global Exposure Mortality Model (GEMM),18 24 30 31 43 47 48 68 77 92 100 the Greenhouse Gas—Air Pollution Interactions and Synergies model,67 72 Intervention Model for Air Pollution67 70 81 and the IMED|HEL model33 57 90 (table 1). Other studies used less known models or developed their own models, such as the GHG-energy-emission-health model developed by Zhang et al73 to assess whether carbon mitigation policies meet their committed peaking goals and bring health cobenefits. Out of all the studies, 16% of them opted for an automated tool called the Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) to facilitate their analysis26 38 44 45 51 54 55 63 75 79 80 87 101 (table 1). All these approaches are commonly used in health risk assessments (HRAs), which in turn, is used in the process of a health impact assessment (HIA). Moreover, three quasi-experimental study methods were mentioned in the selected studies: difference-in-difference design,65 76 interrupted time series76 88 and time-stratified case crossover study.25 These methods are not typically considered as HRA methods, but rather as statistical methods used to evaluate the causal effect of an intervention or policy on an outcome of interest. Only a few articles mentioned using these methods and therefore they will not be discussed further in the review.

Table 1. List of the most common models and tools used in the included studies.

Name of model/tool Description Number of studies
Integrated Exposure-Response (IER) model35,3756 78 100 Developed by Burnett et al116 for the Global Burden of Disease (GBD) study, it is an important exposure-response model that estimates the relative risk of mortality from PM2.5 exposure. It’s widely used by organisations such as WHO in their AirQ+software, the World Bank, and the US Environmental Protection Agency (EPA) in their BENMAP tool.109 The model integrates data from multiple PM2.5 sources, including outdoor and household air pollution, as well as smoking. However, the IER model requires assumptions about toxicity and dosing of particles between sources that have not been fully verified.116 8
Global Exposure Mortality Model (GEMM)18 24 28 30 31 43 47 48 68 77 92 Developed by Burnett et al,107 it is a key tool for modelling the association between PM2.5 and non-accidental mortality. It uses data from 41 cohorts across 16 countries, including recent studies from Asia, and has been applied in global health studies like the GBD. GEMM was designed to address limitations of the IER model, such as its inability to capture nonlinear concentration–response relationships. It also incorporates diverse exposure data, like satellite observations, to enhance accuracy. However, the model is limited in using HR estimates, struggles with risk extrapolation beyond cohort data, and may not represent non-accidental deaths globally.109 11
Intervention Model for Air Pollution (InMap)67 70 81 Developed by researchers at the University of Minnesota, InMap is a multiscale air quality modelling tool that evaluates the health and air quality impacts of emission-reducing interventions. It operates efficiently by using annual-average parameters like transport, deposition and reaction rates from a chemical transport model, simplifying assessments compared with more time-intensive models.117 Validated against other air quality models, InMAP has shown strong accuracy in estimating particulate matter and ozone levels, making it a flexible and powerful tool for city-level and regional-level evaluations. 3
Greenhouse Gas—Air Pollution Interactions and Synergies (GAINS) model67,72 Developed by the International Institute for Applied Systems Analysis, GAINS is an online tool for evaluating cost-effective strategies to reduce air pollutants and greenhouse gases (GHG) while minimising health, ecosystem and climate impacts.118 Using data from energy statistics, emission inventories and national reports, it estimates historical emissions of 10 air pollutants and six GHGs. The GAINS model covers 180 countries/regions globally, with projections from 1990 to 2050 and up to 2070 for Europe. It has also been used to assess health benefits in some studies.67 72 2
Integrated Model of Energy, Environment, and Economy for Sustainable Development (IMED|HEL)33 57 90 Developed by the Institute of Environment and Economy at Peking University, it quantifies the health and economic impacts of ambient air pollution. Widely used in China at both national and provincial levels, it assesses air pollution reduction, human health, resource efficiency and climate policies. The model also includes an indoor air pollution health impact module.119 By providing cost–benefit analyses of energy and pollution control policies, IMED|HEL helps policy-makers evaluate the health and economic benefits of air quality improvements and sustainable development strategies. 3
Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE)26 38 44 45 51 54 55 63 75 79 80 87 101 BenMAP-CE is an open-source tool developed by the U.S. EPA to estimate the health and economic benefits of reducing air pollution. It provides a user-friendly interface for evaluating the public health impacts of air pollution, particularly PM2.5, using preloaded health, demographic and concentration–response data.112 BenMAP-CE estimates health outcomes like premature deaths and assigns a monetary value to these impacts.103 120 While it does not model emissions and relies on external estimates for exposure data, it remains a valuable tool for policy analysis, risk assessment and community health studies. 13
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As evident in the literature, there are several models and tools available that can be used to assess the health impact of air/climate emissions and the cobenefits of reducing those emissions. A short description of the most common models and tools is shown in table 1. This is not an extensive list of all the models and tools available, but of those that were commonly mentioned in the included studies.

Cost–benefit analysis

Cost–benefit analysis (CBA) is a decision-making tool used to evaluate the economic feasibility of a project or programme. It involves comparing the costs of a particular project or programme against its potential benefits in monetary terms to determine whether it is worth pursuing. Costs may include direct expenses such as labour, materials and equipment, as well as indirect costs such as the opportunity cost of resources that could have been used for other purposes. Benefits may include increased revenues, improved health outcomes or other positive outcomes that can be quantified in monetary terms.102

Out of 82 studies, 43 (52%) included an economic assessment of the health co-benefits of the interventions and of these, only 15 also assessed the cost of implementing the interventions analysed (online supplemental figure S3). In other words, only 17.8% of the studies carried out a CBA for emission reduction strategies. The results show that it is economically worth investing in emission reduction strategies. In a study in South Korea, for example, Kim et al42 showed that health benefits alone noticeably offset the costs of cutting GHG emissions. This emphasises the importance of doing CBA to encourage policy-makers to invest in emission reduction strategies. The method for assessing the economic value of health impacts was similar for most of the articles. Several of them used the BenMap-CE tool to assess that. BenMap-CE uses cost-of-illness and willingness-to-pay estimates to value morbidity endpoints; and it uses value of statistical life for mortality.103

Quality of reporting of the included studies

Based on the modified STROBE checklist (online supplemental table S3), the quality of reporting in the included articles varied, with scores ranging from fair (69.6%) to excellent (100%). Our analysis revealed that no studies fell into the poor category (<50%). Only one study26 was considered having a fair quality of reporting, while the remaining studies were considered good (51 studies) or excellent (30 studies). However, it should be noted that certain aspects of the studies were addressed less frequently than others. For instance, approximately 30% of the articles provided a rationale or justification for the study design/framework and described the study setting. Less than 20% of the articles included details regarding the selection of the population group, the reasons behind that selection and the source of population data. Only 25% of the studies described efforts made to address potential sources of bias, while 62% discussed limitations and uncertainties. An exception to these observations is the study by Shi et al,31 which seemingly met all the criteria outlined in the checklist. For the individual quality scores of each study, refer to the supplemental material (online supplemental table S5).

Discussion

The goal of this study was to review the current literature on emission reduction strategies, their health co-benefits and the most common methods and tools available to assess this interaction. We found that the interest in assessing the co-benefits of emission reduction strategies is growing each year, but this growth is not taking place homogeneously across the globe. While two countries (China and USA) lead the way with 38 and 16 studies published on the last 5 years, the majority of countries have less than two or none. This shows how there is a growing need to invest in research that assesses emission reduction interventions in several regions of the world. GHG mitigation actions offer potential co-benefits in the form of improved air quality and public health, which are often the most significant and localised benefits. For cities, especially those in low-income and middle-income countries, seeking solutions that accomplish multiple policy goals at once, these co-benefits can serve as a key point of engagement.104 Kleiman et al67 argue that in areas with limited resources, air pollution and the associated health benefits can be a powerful motivation for cities to pursue more ambitious pollution reduction measures.

The studies included in this review assessed strategies and interventions from different sectors and results show the importance of implementing several combined strategies from different sectors to achieve greater health and economic co-benefits.22 34 48 73 80 This agrees with a former systematic review carried out by Gao et al9 which indicates that comprehensive GHG mitigation measures across various sectors tend to provide greater ancillary health gains. The results from the selected studies also showed that stringent strategies are more effective in producing climate, air and health cobenefits.22 28 50 89

The vast majority of the studies (93%) used PM2.5 as the main parameter to assess health cobenefits of emission reduction strategies, since its CR functions have been very well documented in the literature. In fact, the majority of the studies that address GHG emission reduction use PM2.5 or other air pollutants to carry out the health impact analysis.1828,30 41 63 68 70 Out of all studies included in this review, 45% of them address both air pollutants and at least one GHG, indicating a growing recognition of the importance of addressing climate change and air pollution together.1819 30 51 55 60 70,72 83 Dong et al6 have confirmed the synergistic effect of climate policies on PM emission reduction, with carbon emission reduction accounting for most of the PM2.5 emission reduction.

Climate change and air quality are interconnected challenges that should be tackled synergistically, given their common sources of anthropogenic emissions and the short-term effects of some air pollutants on climate.105 However, not all parameters respond to emission reduction strategies in the same way, and the negative effect of one parameter can offset or decrease the total benefit of the other. For example, some studies have shown an increase in O3 despite the strategies assessed.19 25 36 49 68 These results emphasise the importance of assessing parameters like PM2.5 and O3 together and that careful consideration should be taken of the challenges in coordinated mitigation of both. Therefore, it’s essential to consider as many parameters as possible and to also assess the synergetic interactions between them. Chen and Wang52 acknowledged that climate policies could potentially reduce the levels of other air pollutants not assessed in their study, meaning the actual health benefits could have been greater than estimated. This underscores the importance of taking into account as many variables as feasible, evaluating their synergistic interactions and being cautious when interpreting results.

The majority of studies give evidence that emissions reduction strategies have the potential of improving air quality and human health, reducing mortality and morbidity. Furthermore, studies that quantify health co-benefits in monetary terms suggest that health is a powerful cobenefit and can frequently offset the cost of implementing interventions or strategies.42 In fact, a study by Östblom and Samakovli106 states that health benefits can account for roughly 70%–90% of ancillary benefits, making them a significant representation of such benefits.106 However, it is critical to approach comparisons and interpretations of results with caution since these calculations depend on various input information and assumptions. For example, Kwan et al19 emphasise the need for careful interpretation of results in their study, noting that besides different settings of the electric vehicle transition and energy source scenarios, the scale of health impacts from electric vehicles is influenced by various other factors. These factors include population baseline health status, type of health endpoints evaluated and the willingness to pay for healthcare costs in different countries. As a result, it is necessary to be cautious when interpreting and comparing the results of such studies.

Quantifying the impacts of air quality on human health often involves the use of ERFs, which relate the concentration of pollutants an individual is exposed to over a long period of time to an increased risk of adverse health outcomes such as illness or death, compared with a scenario with zero exposure risk.107 In order to establish an ERF, it is necessary to conduct epidemiological research that involves calculating effect measures, such as relative risks, ORs, or HRs, at various exposure levels (eg, concentration of particulate matter or number of cigarette pack years). These measures are then combined mathematically to determine the extent to which the risk of a particular health outcome increases per unit of exposure to a given risk factor.108 Epidemiological studies of large population cohorts are used to establish these functions. In an optimal case, the ERF is based on a systematic review and meta-analysis of recent studies. However, selecting the most suitable mathematical form of the function is a challenge for it must consider available observations and whether it can be applied to situations outside the studied concentration range or geographical area.107 According to Burnett and Cohen,109 mortality risk estimation has improved over time due to an increase in cohort studies that have led to a better understanding of risk factors. They state that mortality relative risk estimators have become more sophisticated, incorporating multiple independent particulate sources from nearly a hundred studies, instead of previous simple risk models based on a single study.109

The most commonly used methods and tools in the research field for assessing emission reduction strategies and their health co-benefits were reviewed and discussed in this study. It is clear to see that the HRA/HIA approach is widely used, although there can be confusion in regard to definitions and procedures among the two. While several19 22 34 49 82 86 101 of the reviewed articles mentioned performing an HIA or an impact assessment framework, not all of them follow the complete steps of an HIA. Consequently, they are actually carrying out an HRA instead. There are several widely used and trusted models such as the IER and the GEMM, that continue to be useful and relevant in assessing health co-benefits. However, they have limitations and a constant update and improvement in these models is essential. Burnett et al110 discuss the limitations of these two commonly used models, and they suggest that the limitations can be addressed by using a derivative-based model that limits the magnitude of predictions at high concentrations. The authors propose an improved relative risk function that models the association between outdoor fine particulate air pollution and mortality within a meta-analytic framework. The proposed model is a fusion of the log-linear model over low concentrations and functions whose derivatives decline with increasing concentrations. The authors suggest that such supralinear relative risk functions over the entire outdoor air pollution concentration range are supported by empirical evidence from other particle sources such as secondhand smoke, household pollution and active smoking.110

Automated tools such as the BenMap-CE programme have become increasingly popular in conducting HRAs due to their ability to process data quickly and easily. These tools come equipped with preloaded health and demographic data, as well as CR functions, and some allow for user-specified inputs. According to Sacks et al,111 the U.S. EPA’s BenMAP-CE is a well recognised and important tool to assess air pollution and health impacts. The study also assesses the AirQ+tool and shows that the two software packages been used and reported in an extensive number of peer-reviewed publications and technical reports. The range of countries where applications of AirQ+ and BenMAP-CE were performed covers multiple continents, with the most extensive use in Asia, North America, South America and Europe.

It is important to note that while these tools have their advantages, they vary in many aspects, and analysts should carefully choose the tool that best matches the context of the assessment.112 Additionally, balancing the complexity of the information and tools used with the need to produce understandable results for policy-makers and non-technical audiences is a challenging yet crucial task. To effectively communicate the results of the risk assessment to policy-makers and other stakeholders, it is important to present the data in a clear and concise manner that is easy to understand.113

Gaps and challenges in assessing health co-benefits

While carrying out this review, it became clear that, although this is not a new topic, it still needs to be better explored, in order to standardise and improve concepts, methods and tools. Assessing the health co-benefits of emission reduction strategies is not a simple task. It is a complex process that encompasses different disciplines and sectors, and factors that are hard to measure. Pisoni et al114 discuss the challenge that choosing the most appropriate shape of the CR function can be, since ERFs are drawn from epidemiological studies of large population cohorts, and you should consider if the function can be applied for situations that fall outside the observed concentration range or geographical area considered in the epidemiological studies.107 Furthermore, when using models to assess these strategies and their cobenefits, it’s important to keep in mind that models are simplifications that involve assumptions, and these are subject to uncertainties.115

According to Remais et al,115 ‘Uncertainties in modeling health co-benefits include a) simulating the spatial and temporal changes in health-relevant exposures; b) determining the time response of the health effects due to exposure changes; c) comparing alternative mitigation interventions in terms of their health effects across populations and time scales; and d) establishing the assumed time course of future disease-specific burdens in the absence of mitigation’. Another essential point raised by this author is that, when developing models and presenting findings, researchers should work with policy-makers from the beginning to ensure that the questions asked and analyses conducted are policy relevant.115

Another aspect to consider while assessing emission reduction strategies is how well and fully implemented they were, in the case of observed cases or what is the chance of these strategies being well implemented in the case of projected scenarios. We felt that this was not well addressed in the included studies and that it should be better discussed in future research.

An additional challenge can be lack of or difficulty in accessing data. Kleiman et al67 states that the overall lack of local health data further complicates analysis because municipalities without experience in risk communication and public health awareness can have initial hesitancy to share air pollution-related health impacts publicly without a plan to address those impacts or to increase access to healthcare.

We also found that there is a gap in research that assesses health cobenefits taking into consideration aspects of environmental justice. Baghestani et al,23 for example, found that the air quality impacts of cordon pricing scenarios are not evenly distributed. Johnson et al45 found something similar for the health outcome. Across all the policy scenarios, they estimated 10 times more avoided asthma emergency department visits in low-income neighbourhoods as compared with the wealthiest neighbourhoods even though median declines in ambient PM2.5 were similar. Therefore, future research should take this factor into consideration when analysing strategies in different regions and cities.

Strengths and limitations of this review

This is the first systematic review, to our knowledge, to summarise the current studies on emission reduction strategies and the methods and tools that assess their health co-benefits. Measuring the effectiveness of these strategies in producing health and economic cobenefits is growing in importance, considering that decision-makers need cost–benefit information to choose which strategies to implement. This systematic review is also innovative in addressing air pollutant emissions together with greenhouse gas emissions. Current challenges and gaps associated with the health cobenefits assessments of emission reductions were also explored. The literature search for eligible studies was conducted across five databases, ensuring a comprehensive and thorough review of the available research. Additionally, all studies included in the review were classified as ‘high quality’, which enhances the credibility and reliability of the results. This study provides valuable insights into the advantages and disadvantages of various approaches employed for estimating health benefits. The heterogeneity of the included studies, however, poses a limitation on this study. The included articles varied in terms of design, population characteristics, interventions, outcome measures and other factors. Such diversity makes it difficult to interpret the data and limits the extent to which the findings can be generalised. Moreover, this review is limited by the restriction of study selection to articles published in English. This may have excluded relevant studies published in other languages, potentially introducing bias and limiting the comprehensiveness of the findings.

Implications of the results and future research

This systematic review shows that emissions reduction strategies can significantly improve human health, with the potential for health co-benefits to offset intervention costs and motivate action in low and middle-income countries. It highlights the importance of investing in cost–benefit analyses and research in regions with limited studies on emission reduction strategies and health co-benefits. The review suggests that implementing stringent, multisectorial strategies can yield greater health and economic cobenefits. Additionally, it emphasises the need to consider the combined effects of different GHGs and air pollutants in research and models. HRA and HIA were common approaches, although their definitions and procedures may cause confusion. Trusted models like the IER and GEMM were widely used but require regular updates and improvements. The fusion model represents a recent and improved approach. BenMap-CE is an increasingly popular automated tool for HRAs. However, researchers must carefully select the appropriate tool for their assessment context. This review concludes that regular evaluation and enhancement of models and tools are essential for providing accurate and relevant information in health risk and impact assessments.

supplementary material

online supplemental file 1
bmjopen-14-12-s001.docx (34KB, docx)
DOI: 10.1136/bmjopen-2023-083214
online supplemental file 2
bmjopen-14-12-s002.pdf (35.6KB, pdf)
DOI: 10.1136/bmjopen-2023-083214
online supplemental file 3
bmjopen-14-12-s003.pdf (7.1KB, pdf)
DOI: 10.1136/bmjopen-2023-083214
online supplemental file 4
bmjopen-14-12-s004.pdf (7.6KB, pdf)
DOI: 10.1136/bmjopen-2023-083214
online supplemental file 5. (online supplemental file 5).
bmjopen-14-12-s005.xlsx (34.9KB, xlsx)
DOI: 10.1136/bmjopen-2023-083214
online supplemental file 6
bmjopen-14-12-s006.xlsx (27.2KB, xlsx)
DOI: 10.1136/bmjopen-2023-083214

Footnotes

Funding: This work was supported by the GeoHealth Hub for Climate Change and Health in the Middle East and North Africa through funding from the National Institute of Health Fogarty International Center (NIH/FIC) Grant #5U2RTW012228 and # U01TW012237.

Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2023-083214).

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Not applicable.

Ethics approval: Not applicable.

Map disclaimer: The inclusion of any map (including the depiction of any boundaries therein), or of any geographic or locational reference, does not imply the expression of any opinion whatsoever on the part of BMJ concerning the legal status of any country, territory, jurisdiction or area or of its authorities. Any such expression remains solely that of the relevant source and is not endorsed by BMJ. Maps are provided without any warranty of any kind, either express or implied.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Contributor Information

Sammila Andrade Abdala, Email: sammiabdala@gmail.com.

Kenza Khomsi, Email: k.khomsi@gmail.com.

Anass Houdou, Email: ahoudou@um6ss.ma.

Ihssane El Marouani, Email: ihssane.elmarouani@gmail.com.

Imad El Badisy, Email: ielbadisy@um6ss.ma.

Houda Najmi, Email: najmi_houda@yahoo.fr.

Majdouline Obtel, Email: majdobtel7@yahoo.fr.

Lahcen Belyamani, Email: belyamani@gmail.com.

Azeddine Ibrahimi, Email: aibrahimi@um6ss.ma.

Mohamed Khalis, Email: mkhalis@um6ss.ma.

Data availability statement

Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information.

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    Supplementary Materials

    online supplemental file 1
    bmjopen-14-12-s001.docx (34KB, docx)
    DOI: 10.1136/bmjopen-2023-083214
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    bmjopen-14-12-s002.pdf (35.6KB, pdf)
    DOI: 10.1136/bmjopen-2023-083214
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    DOI: 10.1136/bmjopen-2023-083214
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    DOI: 10.1136/bmjopen-2023-083214
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    DOI: 10.1136/bmjopen-2023-083214
    online supplemental file 6
    bmjopen-14-12-s006.xlsx (27.2KB, xlsx)
    DOI: 10.1136/bmjopen-2023-083214

    Data Availability Statement

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