The Social Return Ratio and Behavioral Success from Groundwater Development for Mitigating Against PM2.5 Pollution from Forest Fires in Ko, Li, Lamphun ^†

Abstract

This study aims to evaluate the Ban Ko Groundwater Development Project in Li District, Lamphun Province, which seeks to address PM2.5 pollution from forest fires through rural economic development. The Social Return on Investment (SROI) approach was applied to assess the project’s social return ratio (SRR), revealing that the intervention lacked cost-effectiveness and did not yield sufficient social or economic returns on investment. Decision Tree analysis indicated that economic benefits significantly influenced positive behavioral change toward environmental conservation; however, the magnitude of this change was insufficient to generate substantial environmental improvements. Furthermore, the application of the Collective Interest Model (CIM) revealed that several social factors including personal pro-environmental tendencies, perceived group efficacy, civic responsibility, economic incentives, education, and age contributed to individuals’ decisions to engage in environmental problem-solving. These findings suggest that future economic development efforts must be integrated with social dimensions to foster sustainable environmental solutions in rural contexts.

1. Introduction

Thailand is currently facing environmental challenges from air pollution, PM2.5 [1]. The impact of this issue has been found to have adverse effects on health and be a significant economic burden. According to statistics from the WHO’s International Agency for PM Research on Cancer, high PM2.5 levels, exceeding 5 µg/m³, lead to a 58% increase in mortality rates from heart diseases and stroke, an 18% increase from chronic obstructive pulmonary disease and lower respiratory infections, and a 6% increase from lung cancer in low- to middle-income countries in Southeast Asia, the South Pacific, and the Western Pacific regions [2]. In terms of economics, in the year 2019, PM2.5 pollution caused an economic loss of 2.173 trillion baht for Thai households [3]. The average annual PM2.5 particulate levels from 2019 to 2023 exceeded WHO standards [4]. The main cause of PM2.5 pollution in 2566 was found to be forest fires, accounting for 69.82% of the total, while agricultural activities contributed 26.03%, and other sources like waste disposal accounted for 4.14% [3]. The primary issue causing PM2.5 pollution is forest fires.

In examining the problems and potential solutions, the study selected the area most severely affected in Thailand, where significant resources have been mobilized to address the issue, particularly through development efforts aimed at mitigating environmental impacts. From the situation of forest fires, which are the primary source of the PM2.5 problem, it was found that Ko Subdistrict, Li District, Lamphun Province, has been the most affected village in Thailand over the past 22 years. The forest area of Mae Ping National Park surrounding the village is burned by forest fires more than 20 times per year, with an estimated affected area of over 300,000 rai (over 480 square kilometers) [5,6].

The period when PM2.5 issues occur in Lamphun Province is related to the activities of the local population in the area harvesting forest products. From January to May, the agricultural activities are suspended due to the hot season (March to mid-May), which is not suitable for cultivation, along with the period of harvesting forest resources, including weaver ant eggs (January to early May) and preparing to harvest Pak-wan-pa (Melientha suavis Pierre) (March to May) and puff ball mushrooms (Astraeus hygrometricus (Pers.) Morg) (May to June) at the beginning of the rainy season, generating income from forest products from the people in the area (Figure 1). It can be inferred that the cause of forest fires and PM2.5 pollution stems from people entering the forest to seek benefits.

The villagers who derive economic benefits from forest products identified as a primary cause of forest fires and PM2.5 emissions are classified as forest product harvesters. These can be divided into two main groups: those who engage in harvesting for recreational purposes, which comprises the majority, and those who rely on forest products as a primary source of income, representing a smaller proportion. Regardless of the motivation, the practice of setting fires in forest areas frequently results in uncontrollable wildfires. This is compounded by limited fire management capacity in rural areas, where effective suppression requires substantial labor for firebreak construction and continuous monitoring. Consequently, fire control efforts in the region remain largely ineffective.

Addressing forest fire issues can be approached through short-term regulations and long-term sustainable solutions. One immediate approach to tackling forest burning is the implementation of regulations controlling forest access in specific areas. This strategy ensures that fires are not ignited by local inhabitants. However, such regulations may face resistance due to the lifestyle constraints and poverty in these regions. Implementing rules that reduce local incomes without providing alternative benefits can lead to dissatisfaction and prove unsustainable. Consequently, most measures tend to compromise between the livelihood of the local community and forest conservation. Current strategies focus on creating long-term benefits alongside flexible regulations. This involves building knowledge, understanding, and addressing poverty in the area. Initiatives spearheaded by civil society, such as the “Anandamahidol Foundation Scholarship Recipients Club,” involve both local communities and the government in problem-solving efforts. The primary goal is to alleviate poverty by managing resources, for instance, water and soil, promoting alternative crops, livestock, and fisheries, encouraging tourism, and fostering education. The overall project, known as “Ban Ko Sandbox,” aims to integrate these elements into a comprehensive plan [5,6].

To achieve long-term benefits by building knowledge and reducing poverty to address forest fire issues caused by the harvesting of forest products due to poverty in rural areas through agricultural development. This approach aligns with both development concepts and empirical evidence. In the Ko Subdistrict, as of March 2023, out of 2388 residents [7], 80% are engaged in agriculture. Improving agricultural productivity can be promptly achieved through a sufficient water supply to increase production cycles and improve product quality. Therefore, the “Ban Ko Sandbox” project initiated the “Ban Ko Groundwater Development Project” in Li District, Lamphun Province, to address these issues and serve as a case study.

The hypothesis is that addressing environmental problems by advancing economic development can increase public interest and awareness of these issues. This concept supports the notion that innovation can simultaneously reduce emissions while maintaining production costs [8]. Moreover, higher levels of economic development are often associated with lower levels of environmental degradation [9]. However, environmental policies can also impose significant constraints on economic growth [10]. Despite ongoing debates over the balance between environmental protection and economic advancement, promoting economic development remains essential for the sustainability and viability of rural areas.

To examine the validity of project-level hypotheses, this study emphasizes the importance of evaluating the long-term environmental impacts of rural economic development. Central to this evaluation is an analysis of the processes that signify project success in terms of both value creation and behavioral decision-making. Accordingly, the study is guided by two principal research objectives: (1) to investigate the extent to which investment in large-scale groundwater infrastructure yields outcomes that reflect the SRR, thereby demonstrating both efficiency and effectiveness within the framework of social value evaluation; and (2) to examine changes in environmental behavior resulting from the receipt of economic benefits, and to identify the key determinants that significantly influence the project’s role in fostering such behavioral change.

Therefore, the processes and findings of this study may be adapted to the context of other countries or regions, particularly those that are rural or developing. Such applications include project evaluation of social investment aimed at addressing economic and environmental problems, thereby supporting decision-making for more efficient allocation of public resources. In addition, by advancing the understanding of environmental behavior and the factors influencing decision-making, this study provides a foundation for more comprehensive policy recommendations to address these challenges in the future.

2. Literature Review

This article explores several interrelated dimensions, including environmental degradation driven by rural poverty, economic development, and the application of analytical methods to evaluate the efficiency, effectiveness, and success of development projects in fostering behavioral change following the receipt of economic benefits.

2.1. Forest Fires Driven by Poverty: The Role of Economic Development in Long-Term Prevention

In northern Thailand, forest fires are often associated with local livelihoods, especially among low-income household’s dependent on forest resources. Many villagers use fire as a tool to stimulate the growth of economically valuable non-timber forest products, including wild mushrooms, bamboo shoots, and medicinal plants. This practice is economically driven rather than purely cultural or ecological, as these forest products can represent a substantial portion of household income [11].

Although burning is prohibited in community forest areas, enforcement has a limited effect, particularly where poverty continues to drive the need to collect and sell forest products. A study in northeastern Thailand found that non-timber forest products contribute approximately 10% of household income in rural areas, and 21% of forest product collection is explicitly market-oriented [12]. These findings suggest that environmental regulations may have limited influence if economic alternatives are not available to forest-dependent communities.

Community forest programs have been introduced in Thailand to promote participatory forest governance, aiming to balance conservation goals with local livelihoods. However, these programs often fall short in addressing the root cause of fire use: rural poverty and the lack of alternative income sources [13]. While institutions for local forest governance may exist, economic incentives for behavior change remain insufficient.

While several studies have identified poverty as a structural driver of forest fire incidence in Northern Thailand particularly through fire-based practices for collecting non-timber forest products there is limited empirical research assessing whether rural economic development can reduce such fire-dependent behaviors. The lack of longitudinal or project-based evaluations means that the effectiveness of interventions such as groundwater development or sustainable livelihoods in reducing burning practices remains unclear in the Thai context.

2.2. Economic Development as a Foundation for Rural Poverty Alleviation

The literature strongly supports the view that sustainable rural economic development is a foundational approach to alleviating poverty. Empirical studies demonstrate that investments in rural infrastructure, agricultural productivity, and financial services have a direct impact on increased incomes, reduced food insecurity, and decreased migration pressure [14,15]. Agricultural-led growth, in particular, is recognized as more pro-poor than growth in other sectors [16]. Fundamentally, this underscores the role of development programs—particularly those focused on irrigation as having demonstrated significant impacts on poverty reduction across Asia [17].

Although the concept of economic development increasingly incorporates environmental considerations [18], relatively few studies have explicitly examined the linkage between rural economic development and environmental problem-solving. There remains a gap in the literature concerning evaluative frameworks that assess whether such development initiatives can effectively address environmental issues in rural areas.

2.3. Social Return Ratio as an Indicator of Efficiency and Effectiveness in SROI Evaluation

Social Return on Investment (SROI) has emerged from traditional cost–benefit analysis (CBA) as an advanced framework to measure and account for broader notions of value, especially social and environmental impacts often overlooked in financial assessments [19,20]. By emphasizing stakeholder engagement and incorporating intangible outcomes [21], SROI facilitates a multidimensional understanding of project performance. This includes not only financial returns but also the well-being of affected individuals and communities, environmental sustainability, and long-term systemic change. The framework achieves this by assessing changes in people’s lives or organizational behavior through a narrative of change, using monetary proxies to capture value, and calculating numerical outcomes that reflect the social impact of interventions [22].

Within the SROI framework, the Social Return Ratio (SRR) has gained attention as a key indicator for assessing both the efficiency and effectiveness of development projects. SRR quantifies the amount of social value generated per unit of investment, functioning as a proxy for economic efficiency while capturing broader societal impacts. Millar and Hall (2013) emphasize that SROI enables program evaluations to move beyond narrow financial metrics, supporting more strategic and efficient resource allocation [23]. Similarly, Arvidson et al. (2013) critique traditional evaluation methods for neglecting intangible and long-term impacts, and argue that SROI better reflects the effectiveness of interventions by including stakeholder well-being and systemic changes [24].

Despite its growing application, SROI still faces methodological challenges that may constrain its use in complex, rural, or environmental settings. Key limitations include the difficulty of selecting appropriate monetary proxies for non-market outcomes, quantifying long-term environmental benefits such as PM2.5 reduction, and ensuring data validity in informal or resource-limited contexts [22,23]. Furthermore, the existing literature has not adequately explored the use of SROI particularly SRR as a tool for evaluating integrated development projects that aim to address groundwater access, poverty alleviation, and forest fire prevention. This highlights an empirical and methodological gap that this study aims to address by adapting the SROI framework to assess both the efficiency and effectiveness of environmental–economic interventions in rural Thailand.

2.4. Assessment of Project Success Through Behavioral Change

Behavioral change is increasingly regarded as a fundamental indicator of the success of environmental interventions, particularly when it reflects a shift away from environmentally harmful practices toward sustainable alternatives. In rural development contexts, behavioral transformation such as reductions in burning or improved waste management often results from interventions that alter risk perception, enhance livelihood security, or build environmental awareness [25]. Similar outcomes have been reported in projects promoting sustainable agriculture or eco-tourism, where beneficiaries changed practices not only due to knowledge gains but also because of direct economic incentives [26].

Decision Tree analysis has been widely applied as a decision-support tool to model behavioral change in environmental contexts. It allows researchers to classify individuals based on key variables and to identify the most influential predictors of pro-environmental behavior. For example, Battista et al. (2022) applied a Decision Tree model to survey data from Canadian adolescents, revealing that social norms, environmental identity, and perceived collective benefits were the strongest predictors of youth engagement in recycling and energy-saving behaviors [27]. Similarly, Wang et al. (2022) used a Decision Tree approach to examine pro-environmental behaviors among Chinese university students [28].

One conceptual framework that explains and supports the emergence of pro-environmental behavior is the Collective Interest Model (CIM), which posits that individuals are more likely to engage in environmental actions when they perceive strong personal efficacy, group efficacy, and a sense of alignment between individual and collective outcomes. Lubell et al. (2007) found that these factors significantly predict policy support and voluntary environmental behavior [29]. In the rural Thai context, Janmaimool and Denpaiboon (2016) demonstrated that place identity, self-efficacy, and social norms are key determinants of conservation and waste-related practices [25]. Similarly, Wild (2024) demonstrated that social identity plays a more significant role in predicting activist behaviors, while personal identity is more strongly associated with consumer-oriented pro-environmental behaviors among young adults, emphasizing the importance of identity formation in fostering environmental engagement [30].

Despite these insights, existing studies have not fully integrated CI model variables into Decision Tree frameworks to evaluate behavior change resulting from rural development interventions. There remains a methodological gap in systematically quantifying the magnitude of behavioral change and isolating the specific project-induced factors that influence these changes. In Thailand, such integration is particularly lacking in studies assessing rural infrastructure projects such as groundwater development despite their potential for influencing both livelihoods and environmental practices.

2.5. Contribution and Innovation

This research contributes to the existing literature by addressing a critical gap in understanding how poverty-driven forest dependence contributes to PM2.5 emissions from forest fires in rural Thailand. By focusing on a context-specific case, the study demonstrates how groundwater-based agricultural development can function as both an economic and environmental intervention. The methodological innovation lies in the adaptation of the SROI framework to simultaneously evaluate cost-effectiveness and effectiveness, capturing not only the financial but also the broader socio-environmental outcomes of rural development projects. This dual perspective advances current evaluation practices, offering a more comprehensive tool for assessing interventions that aim to reduce poverty while mitigating environmental degradation.

In addition, the study advances methodological rigor by integrating the CIM into a Decision Tree framework to examine behavioral changes arising from the intervention. This integration enables a more systematic identification of the underlying drivers such as self-efficacy, group efficacy, and economic incentives that influence community decisions toward pro-environmental practices. By combining behavioral theory with predictive modeling, the research provides a novel analytical pathway to link development-induced benefits with long-term environmental sustainability. This contribution not only strengthens causal explanations but also informs the design of future interventions aimed at aligning rural economic development with sustainable environmental outcomes

3. Materials and Methods

3.1. Project Overview

The groundwater development project is a large-scale investment in agricultural and consumption infrastructure, encompassing 37 sites and covering 1975 rai (3.16 square kilometers) of agricultural land, benefiting 2996 people, as shown in Figure 2. The primary objective of the project is to increase income and reduce living costs. The simple assumption is that by increasing the quantity and quality of agricultural products and reducing living costs through access to clean water for consumption, the reliance on forest product harvesting will decrease. This is because farmers can engage in agriculture year-round to generate income. Such activities will reduce forest encroachment, which is a cause of forest fires, and create long-term benefits that support the implementation of measures or regulations, such as prohibiting forest product harvesting.

The project’s costs (

Table 1. Details and costs of the groundwater drilling sources in each area of the Ban Ko community, Li District, Lamphun Province.

Sub-Projects	Objective	Number	Survey Costs	Construction and Water Distribution Systems Costs	Total
Groundwater development project, single-purpose type	Agricultural	16	51,200.00	442,500.00	493,700.00
Groundwater development project, dual-purpose type	Agricultural	17	51,200.00	660,000.00	711,200.00
Groundwater development project for Farmers using Solar Energy (M1, M3)	Agricultural	2	1,197,726.00	6,950,074.00	8,147,800.00
Groundwater development project for community stability	Consumption	1	629,300.00	4,577,500.00	5,206,800.00
Large-scale groundwater development project to solve draught problems under the royal initiative	Consumption	1	6,547,140.00	24,526,000.00	31,073,140.00

) cover five sub-projects. The two agricultural sub-projects of the “Groundwater Development Project, Single-Purpose Type” and the “Groundwater Development Project, Dual-Purpose Type” incur relatively low capital expenditures and are dispersed across floodplain farmlands to increase the number of annual fields-crop production cycles. In contrast, the “Groundwater Development Project for Farmers Using Solar Energy (M1, M3)” entails higher investment and delivers greater technical performance; it is sited in principle off-community horticultural zones to enhance both the frequency of production cycles and the quality of orchard yields.

The consumption-oriented sub-projects include the “Groundwater Development Project for Community Stability” which represents a high-cost water source supplying clean drinking water to both residential areas and tourist sites, and the “Large-Scale Groundwater Development Project to Address Drought under the Royal Initiative” a similarly high-cost source that integrates with existing community water infrastructure and the municipal water supply.

3.2. Evaluation of Social Return Ratio

The Social Return Ratio (SRR) for the Ban Ko Groundwater Development Project in Li District, Lamphun Province, was calculated using the SROI analytical framework. Data were collected between 9 March 2023 and 30 November 2023 using in-depth interviews, documents, and statistical averages, particularly market prices of agricultural products and appropriate financial proxies. These data underpin the subsequent analytical steps, as illustrated in Figure 3.

This process helps evaluate efficiency, while effectiveness is measured through the ability to achieve results that align with the project’s realistic changes. It requires integrating qualitative data with quantitative analysis.

3.2.1. Identifying Key Stakeholders

Identifying key stakeholders is essential, as they are individuals or organizations that experience change due to the analyzed activity, making their input vital for understanding the value created or diminished [22]. Key stakeholders, defined as those significantly affected by the activity, play a crucial role in assessing the project’s overall value and impact [31]. The identification process typically begins with a stakeholder analysis, which systematically maps all relevant parties influenced by the activity to ensure a comprehensive understanding of the stakeholder landscape. These stakeholders may include direct beneficiaries, their families, local communities, and even state-level institutions, highlighting the importance of considering multiple layers of influence [31,32].

3.2.2. Mapping Outcomes

Mapping outcomes involves identifying and documenting the effects of a project or intervention on various stakeholders, which is crucial for understanding its overall impact [22]. This step refers to the understanding of the Theory of Change, a conceptual framework utilized for measuring social impact. It serves as a process or method for project planning, focusing on validating assumptions and hypotheses regarding intervention mechanisms or project operations. The approach emphasizes comprehending how specific impacts are achieved and under what conditions, relying on empirical evidence and systematically gathered findings from relevant fields or target groups. Additionally, the Logic Model is employed to visually summarize the causal linkages between inputs, activities, outputs, outcomes, and impacts [33].

3.2.3. Evidencing Outcomes and Giving Them a Value

Valuing outcomes is essential as it quantifies the benefits generated by the intervention, allowing stakeholders to understand the social value created and facilitating accountability and future funding opportunities [34]. The process of evidencing outcomes involves collecting data to demonstrate whether specific outcomes have occurred as a result of an intervention, which is a critical step in the SROI framework [22]. This stage is complex and requires extensive fieldwork, including the design of tailored surveys for different user groups to capture their experiences and reported outcomes [35]. Once outcomes are evidenced, the next step is to assign a monetary value to these outcomes using financial proxies, which can be derived from various sources, including primary data and existing literature on social impact measurement [31].

In order to ensure the reliability of financial proxies, the researcher uses Techniques Economics Valuation to consider the value of the outcomes based on an individual’s Willingness to Pay for services or the use of environmental resources. The valuation methods are shown in

Table 2. Taxonomy of relevant valuation techniques adapted from “Environmental Economics and Valuation in Development Decision-making” (1992) [36].

	Conventional Market	Implicit Market	Constructed Market
Based on actual behavior	Change in Productivity Los of Earnings Defensive Expenditure	Travel Cost Wage Differences Property values	Artificial market
Based on potential behavior	Replacement Cost Shadow project		Contingent valuation

, depending on the type of market being relied upon, and consider whether they utilize actual or potential behavior [36]. The process outlined in Table 2 was used to identify financial proxies for outcome valuation, drawing from direct stakeholder interviews, documents, and statistical data particularly market prices of agricultural products.

The process outlined in Table 2 was used to identify financial proxies for outcome valuation, drawing from direct stakeholder interviews, documents, and statistical data particularly market prices of agricultural products.

3.2.4. Establishing Impact

Establishing impact requires a systematic approach to evaluating the extent to which an intervention generates meaningful change for stakeholders while accounting for external influences [35]. The SROI framework provides a rigorous methodology for isolating the intervention’s true impact by addressing key factors such as deadweight, attribution, and displacement [35]. This approach aligns with the following Formula (1).

$$NPV={\sum }_{t=1}^n\left({Proxy Financial}_j\times {Outcome}_j\right)\times {\displaystyle \frac{1}{{Deadwe}_j}}\times {\displaystyle \frac{1}{{Displ}_j }}\times {\displaystyle \frac{1}{{Attr}_j}}\times {\displaystyle \frac{1}{{DropOff}_j}}\times {\displaystyle \frac{1}{{\left(1+r\right)}^t }}$$

(1)

At this stage, the future value of each outcome is estimated, followed by the calculation of its Net Present Value (NPV). The NPV of outcome j is calculated over n years, where t denotes the time period and 1/(1 + r)^t represents the discount factor applied to reflect the time value of money [37].

Deadweight (Deadwe) measures the proportion of outcomes that would have occurred irrespective of the intervention, ensuring that only the additional value created is considered [16]. Displacement (Displ) examines whether the benefits realized come at the expense of other groups, thereby preventing misrepresentation of net impact [18]. Attribution (Attr) assesses the degree to which observed outcomes can be directly linked to the intervention, distinguishing them from effects caused by other factors [35]. The drop-off (DropOff) indicates the reduction in the impact across time [37]. Expressing impact in percentage terms facilitates a clearer and more precise representation of the factors shaping the overall value [22]. The ratios were determined through consultations with stakeholders.

The calculated NPV represents the net impact generated, reflecting the project’s overall effectiveness. Sensitivity values of 3%, 7%, and 10% were applied as discount rates, based on recommended sensitivity ranges for water-related projects in developing countries [38].

3.2.5. Calculating the SROI

The SROI calculation is derived from the NPV of outcomes encompassing economic, social, and environmental dimensions divided by the NPV of the investment, as presented in Equation (2).

$$SROI={\displaystyle \frac{Net Present Value of Outcomse}{Net Present Value of Investment}}$$

(2)

The created value, or outcome, should correspond to the investments made and is typically represented as a ratio. An SROI ratio of 3:1, for example, signifies that for every baht invested, the organization generates three baht of social value, net of costs [34]. In other words, an SROI > 1 reflects a SRR indicating that the project is effective and delivers value beyond its cost.

3.3. Project Success Assessment

The primary objective of this stage is to assess the effectiveness of environmental problem-solving through economic development, with a focus on the primary beneficiaries of the project namely, farmers. Data collection was conducted using structured questionnaires distributed to a target population of 184 individuals, from which 154 responses were obtained between 31 October and 30 November 2023. The analysis was divided into two principal components.

3.3.1. Decision Tree

The decision tree is a rapid and efficient classification prediction algorithm commonly utilized in data mining for decision-making purposes. It is classified as a supervised learning method that seeks to identify the relationship between input attributes and target attributes, representing this relationship through a structured model [39].

Decision Tree analysis employs decision criteria based on behavior, following the methodological framework of decision tree diagrams to develop decision-support tools for assessing the potential for success in cases or efforts involving change. The criteria are defined according to the specifications outlined in Figure 4.

To analyze the success of the project under the given hypothesis, it is necessary to refer to the original behaviors that cause environmental problems (forest burning). The analysis aims to determine whether the use of groundwater for economic development can stop the harvesting of forest products, based on the project’s hypothesis. Success here is defined as “the project’s ability to change the behavior of the sample population, who are the primary beneficiaries of the project (farmers using groundwater from the project), to cease harvesting forest products.” The reason for using forest entry behavior, assessed by frequency, is that forest burning is illegal, and people will not answer questions directly and will avoid providing information. Additionally, forest fires tend to spread widely, even if the number and frequency of people entering the forest are low. Therefore, a reliable indicator that the local population does not contribute to environmental degradation is that the project beneficiaries either refrain from harvesting forest products or are able to cease foraging activities in the forest.

3.3.2. Collective Interest Model: CIM

The CIM integrates the demand for public goods into an individual’s utility calculus while addressing the logic of free-riding [40]. The model posits that individuals engage in air policy activism when the subjective expected value of participation is positive. This expected value is influenced by five key factors: (1) the perceived value of the collective good resulting from successful environmental action, (2) the increased probability of success if the individual contributes, (3) the extent to which the group’s collective actions are likely to succeed, (4) the selective costs associated with participation, and (5) the selective benefits derived from participation. Selective benefits and costs encompass material, social, or psychological outcomes exclusive to active participants [29]. The fundamental relationship among these factors is expressed as:

$$EV(Environmental problem solving)=\left[\right(p_g \ast p_i) \ast V] - C+B$$

(3)

where EV (environmental problem solving) is the expected value of participation, p_g is the probability that the group will be successful (group efficacy), p_i is the marginal influence of the individual’s contribution on the probability of success (personal efficacy), V is the value of the collective good, C is the selective cost of participation, and B is the selective benefit available from participation [41].

Finkel and Muller (1998) [40] emphasized the roles of V, p_i and p_g collective interest variables crucial for overcoming free-riding behavior. While an individual’s contribution may marginally increase the probability of a successful public good, personal perception of influence on collective outcomes remains pivotal. The perceived personal efficacy (p_i) tends to increase with individual belief in their contribution’s effectiveness. However, the model highlights that in larger groups, pi is reduced, leading individuals to underestimate their influence, thus reinforcing free-riding tendencies [41].

Contrarily, the Collective Interest Model posits that individuals may overestimate their personal efficacy, resulting in heightened participation compared to Olson’s prediction. Moreover, this model incorporates group efficacy p_g alongside personal efficacy, acknowledging that even ineffective group actions may foster participation if perceived benefits outweigh the costs. Consequently, the likelihood of participation rises with increased perceived efficacy and collective benefit potential [42].

The CIM was applied to identify the determinants of pro-environmental behavioral change that reflect the success of project interventions in addressing environmental issues. A close-ended questionnaire was utilized to collect data on the perceived importance and frequency of environmental behaviors, using a rating scale. Binary logistic regression was conducted to assess the statistical significance of predictive factors among the sample population. The dependent variable (EV) represents the behavioral outcome, categorized as either ‘Able to stop harvesting forest products’ or ‘Unable to stop harvesting forest products’ after receiving project benefits. Independent variables were constructed based on the CIM framework, including perceived environmental risks (V), particularly forest fires and PM2.5; pro-environmental intention (p_i); perceived group efficacy and civic responsibility (p_g); environmental value orientation (B); and control variables (C), comprising income, education, age, and knowledge regarding the environmental consequences of forest product harvesting.

4. Results

4.1. The Project’s Social Return Ratio

4.1.1. Key Stakeholders

In this paper, key project stakeholders who serve as data providers—in the case of population groups or organizations will have representatives responsible for carrying out activities and monitoring outcomes interviewed. These representatives are those who are likely to be affected after the project’s implementation or who are involved in driving the change. This will be combined with actual data from field surveys and documents to study the magnitude of the changes within the Ban Ko, Li District, Lamphun Province, as detailed in

Table 3. Definition of key stakeholders.

Key Stakeholders	Count	Definition
Farmer groups	184	A group of farmers engaged in agricultural activities with an area of 386.54 rai that can utilize groundwater from the project during the summer, cultivating approximately 11 types of crops.
Water-consuming Population	2996	A group of the general publics in the community residential area who use groundwater from the project for consumption, which can support the consumption of 2996 people out of the total population.
Ban Ko Subdistrict Municipality Office	1	A local government agency is responsible for the Ko sub-district, Li district, Lamphun province, with the legal duty to care for and improve the living conditions of people in its area of responsibility. It also manages and oversees public benefits arising from the project.
Bureau of Groundwater Resources Region 1 Lampang	1	A regional government agency responsible for controlling, supervising, monitoring, surveying, assessing the potential for development, promoting the utilization of groundwater, and managing groundwater resources for maximum benefit. It is responsible for 8 upper northern provinces of Thailand.
Civil society from the “Ban Ko Sandbox Project”	1	A non-profit civil society organization that addresses social needs or problems. The “Ban Ko Sandbox Project,” supported by the “Ananda Mahidol Foundation Scholarship Recipients Club,” aims to solve environmental problems related to PM2.5 and reduce forest fires by addressing the livelihoods of people in the area.

.

The stakeholder relationships consist of: civil society from the “Ban Ko Sandbox Project,” the initiator of the PM2.5 from forest fires solution who develops the framework for development and seeks cooperation and coordination with both the Ko Subdistrict Municipality Office, a local government agency with a modern democratic decentralization structure, and the Bureau of Groundwater Resources Region 1 Lampang, a regional agency in a form of decision-making and budget allocation from the central government. These first three parties play a role in developing, monitoring, and controlling the allocation of benefits from the project to the beneficiaries, which include the agricultural group, the ultimate beneficiaries through the use of groundwater resources for agriculture from the project, and the people who use water for consumption.

4.1.2. Outcome Map

The outcome mapping of the groundwater development project begins with inputs, which are the components that enable activities to be carried out. These require human capital including skills, expertise, knowledge, and labor, combined with government support that facilitates resources in terms of finance, licensing, and support in terms of knowledge and tools for exploration and use in the construction of infrastructure. In order to bring natural resources, groundwater sources, to use for agriculture and consumption. Each group of stakeholders performs activities according to their duties and benefits, as shown in Figure 5.

They are divided into two groups. First is the project implementers, consisting of civil society that designs concepts, policies, and activity plans to solve the PM2.5 problem, with the Bureau of Groundwater Resources Region 1 Lampang, which supports and controls the construction, together with the Ko Subdistrict Municipality, which is the government sector that facilitates and oversees the benefits generated by the project for the people. The second group, the beneficiary group, includes the agricultural group that benefits from groundwater for agriculture, and the groundwater consumers, especially for daily consumption.

The outputs resulting from the activities, specifically from the project implementers, primarily include environmental (PM2.5) solutions, which are linked to the amount of water managed for farmers and people who use water for consumption. Increased water availability, a critical factor in agriculture, leads to increased agricultural productivity and increased water consumption for beneficiaries, resulting in increased land use and increased agricultural waste as well.

The outcomes that align with the project’s objectives are that increased agricultural production leads to increased income, while people can consume clean water, which results in reduced living expenses. On the one hand, the improved well-being of the people from the project comes from government support for the cost of groundwater management. On the other hand, the problem of forest fires from the beneficiary group is reduced because farmers can earn income during the off-season, so it is not necessary to harvest products from the forest, resulting in a reduction in forest fires and PM2.5. Meanwhile, unexpected outcomes result in increased soil degradation, requiring budget allocation for agricultural waste disposal to prevent PM2.5 from agriculture.

The resulting impacts are divided into three areas based on the outcomes of the project and the concept tree of sustainability [43]. In the economic aspect, it reduces poverty and promotes economic growth. In the social aspect, the project has created guarantees and security in water management, social welfare, and strong communities. In the environmental aspect, although it has a negative impact on the use of resources for agriculture, it creates a positive impact on forests and improved air quality.

4.1.3. Evidence of Outcomes

According to the SROI assessment following an Ex-ante approach, the project had begun in 2022 and it was completed and ready to launch in 2023; this research forecasts the potential impacts up to 2027. The process begins by identifying the indicators of the outcomes and using Financial Proxies and calculations to determine the value of the outcomes, as shown in

Table 4. Financial proxies and calculating the annual value of outcomes.

Outcomes	Financial Proxy and Calculating	Value (THB)
Increased agricultural income	Using Change in Productivity [36] of increased agricultural output per rai, totaling 189.96 rai (303,936 square meters), and the market price (average price) of each type of output, the estimated total is: - Data: 8866.52 THB/rai (5.54 THB/square meter) - Forecasted Data: 10,520.23 THB/rai (6.58 THB/square meter)	1,684,284.48 and 1,998,423.74 *
Increased agricultural land degradation	Using Replacement Cost [44] to restore soil degradation, based on the average price of organic fertilizer at 10 THB/bag (10 kg) for both field crops and horticultural crops in the area: - Data: Field Crops: Estimated area of 25.25 rai (40,400 square meters), using approximately 3787.5 bags of organic fertilizer, with a total restoration cost of 37,875 THB/year. - Forecast: Field Crops: Estimated increase in agricultural area in 2025 by an additional 29.75 rai (47,600 square meters), using approximately 4462.5 bags of organic fertilizer, with a total restoration cost of 44,625 THB/year. - Forecast: Horticultural Crops: In 2023 and 2025, an estimated area of 134.96 rai (215,936 square meters), using approximately 8435 bags of organic fertilizer. Considering the project’s impact proportion of 33.34%, the total restoration cost is 28,122.29 baht/year.	−65,997.29 and −110,622.29 *
Increased agricultural waste	Using Defensive Expenditure [44] for plowing under crop residue instead of burning, at a cost of 450 THB/rai. - Field Crop Area: 25.25 rai (40,400 square meters) - Forecasted Field Crop Area: Approximately 55 rai (88,000 square meters)	−11,362.5 and −24,750 *
Reduced costs of purchasing clean water for public consumption	Using the Market Price [45] of clean drinking water at 2.91 THB/liter: - Data: Average consumption of 593,855 L/year = 1,728,118.05 THB/year - Forecasted Data: Estimated average consumption of 1,301,955 L/year = 3,788,689.05 baht/year - Data: Cost of water for domestic use at 11 baht/cubic meter × volume of water used by the public from the project 31,555.9 cubic meters/year = 347,111.60 THB/year	2,075,229.65 and 4,135,800.65 *
Improved public well-being from support for groundwater management expenses	Using the cost of groundwater management based on electricity costs for water pumping, categorized by project as follows: (1) Groundwater Development Project for Farmers using Solar Energy, M3: 17,523.504 cubic meters/year × water management cost of 2 baht/cubic meter = 35,047.008 THB/year (2) Groundwater Development Project for Farmers using Solar Energy, M1: 20,677.73 cubic meters × water management cost of 2 THB/cubic meter = 41,355.46 THB/year (3) Groundwater Development Project for Community Security: 416.83 cubic meters/year × water management cost of 3 THB/cubic meter = 1250.49 baht/year (4) Large-Scale Groundwater Project to Solve Drought Problems Due to Royal Initiative: - Data: Water Consumption 177.025 cubic meters/year × water management cost of 3.5 THB/cubic meter = 619.59 THB/year - Forecasted Data: Water Consumption 885.125 cubic meters/year × water management cost of 3.5 baht/cubic meter = 3097.94 THB/year - Data: Water for Domestic Use 31,555.9 cubic meters/year × water management cost of 3.5 baht/cubic meter = 110,445.65 THB/year	−188,717.15 and −191,195.50 *
Interaction between the government, civil society, and the public	Using the added value from the participation of 10 community members involved in developing the guidelines and 10 community members benefiting from the project, for 4 sessions, including meeting management costs.	98,273.00
Reduced forest fire problems	Using Defensive Expenditure [44] of the local government budget allocated for forest fires prevention per capita, at an average rate of 20.21 THB/capita, for 184 participating residents aged 25–60 years, which decreased after participating in the project.	3718.64

One Rai is equal to 1600 square meters. * Projecting outcome beyond the evaluated year (2025).

.

Considering the Financial Proxies, which are the values of the outcomes resulting from the project, the process starts with considering primary data analytics from stakeholder interviews as the main criterion. Then, secondary data related to market prices (average prices in the year) and documents from the government are used to support the valuation. The criteria for reliability and the closest possible reference in the area are used for that period. The data sources must align with Economic evaluation techniques to support the decision to select the value, in order to avoid highly misleading data and tools.

In this paper, the assessment is divided into two parts: the part that evaluates the outcomes (Ex-post) that have already occurred from the project’s start in 2022 to the project evaluation year of 2023, and the part that forecasts the impacts (Ex-ante) that will occur in the future (2025), which is the year when the project’s efficiency creates the highest value of outcomes. The reason for using both approaches is that some agricultural products take several years to grow and yield results, which stakeholders estimate will occur in 2025.

4.1.4. Establishing the Impact

This process aims to extract the value of the outcomes from other factors. This rate will be deducted from the value of the outcomes calculated in the previous step, which includes the Deadweight. In this context, Deadweight refers to the fact that the outcomes from the project would still occur without the project implementation. The value of the outcome from the reduction in forest fires is assigned a Deadweight of 33%. This means that the population group that benefits from the project at that rate has never entered the forest, so there is no chance of creating an impact such as a forest fire. The value of the outcome in this part is therefore not used in the calculation.

Regarding Displacement, it does not appear that any outcome leads to a negative impact or is considered a negative outcome, so double-counting is avoided. Attribution, the increase in agricultural income from the project is at a rate of 0%, meaning the project has enough water to increase agriculture or improve product quality to a higher price without relying on any project to create that outcome. In case farmers choose to grow familiar crops such as longan, sweet corn, and feed corn. Meanwhile, a dependence rate of 20% is applied to agricultural crops that are domestic crops and require some techniques to increase production, including chili, mango, eggplant, coconut, and guava. A dependence rate of 30% is applied to those who require assistance from other government agencies to help with planning and guidance to achieve results. The rate of dependence on interaction between the government sector, civil society, and the public is 50% due to the environmental problem-solving and community development approach, as civil society relies on information and budgets from many parties to carry out activities.

Finally, there is Drop-off, which refers to the rate at which the value of the outcome will decrease each year. The increase in agricultural income is set at 10% due to maintenance costs and reduced efficiency leading to decreased income. Meanwhile, the rate of dependence on the interaction between the government sector, civil society, and the public is as high as 50% because, after the project is completed, the interaction of the people involved in the project will decrease in frequency on various issues. As shown in

Table 5. Determining the project’s impact.

Outcomes	Deadweight	Displacement	Attribution	Drop-Off	Value (THB)
Increased agricultural income	0%	0%	0%, 20%, 30%	10%	1,659,579.33 and 1,948,666.77 *
Increased agricultural land degradation	0%	0%	0%	0%	−65,997.29 and −110,622.29 *
Increased agricultural waste	0%	0%	0%	0%	−11,362.50 and −24,750.00 *
Reduced costs of purchasing clean water for public consumption	0%	0%	0%	0%	2,075,229.65 and 4,135,800.65 *
Improved public well-being from support for groundwater management expenses	0%	0%	0%	0%	−188,717.15 and −191,195.50 *
Interaction between the government, civil society, and the public	0%	0%	50%	50%	49,136.50
Reduced forest fire problems	33%	0%	0%	0%	1243.89

* Projecting outcome beyond the evaluated year (2025).

.

The assessment of the impacts of the groundwater development project in Ban Ko, Li District, Lamphun Province, from 2022–2027, has values in years 0–5. Setting sensitivity values at 3%, 7%, and 10% according to the sensitivity of water-related project values in developing countries, results in a total impact value or return on investment of 20,591,752.19 THB, 18,280,321.85 THB, and 16,796,064.08 THB.

4.1.5. Social Return Ratio

Calculating the project’s outcomes based on sensitivity (20,591,752.19 THB, 18,280,321.85 THB, and 16,796,064.08 THB)/the project’s cost of 45,632,640 THB, the SROI ratio is equal to 0.45, 0.40, and 0.37. The findings indicate that the project has not yet demonstrated operational efficiency within the evaluated timeframe.

4.2. The Assessment of Success in Terms of Behavioral Change from the Project

4.2.1. The Assessment of Success in Terms of Population Size

Based on the analysis of the magnitude of change using a Decision Tree to study the role of the project in intervening in the aspect of behavioral intervention, where “the ability to stop harvesting forest products” is defined as an indicator that confirms that the sample group benefiting from the project will not cause environmental impacts (PM2.5 from forest fires), as shown in

Table 6. Evaluating the Decision Tree.

Original Behavior	Project Utilization	Changed Behavior
Enters the forest 1–3 times/year (N = 96), 62.34%	Utilize (N = 21), 21.875%	Changed (N = 15), 71.43% ***
		Not changed (N = 6), 28.57% *
	Not utilize (N = 75), 78.125%	Changed (N = 36), 48%
		Not changed (N = 39), 52%
Enters the forest frequently (N = 7), 4.54%	Utilize (N = 3), 42.857%	Changed (N = 2), 66.67% ***
		Not changed (N = 1), 33.33% *
	Not utilize (N = 4), 57.143%	Changed (N = 2), 50%
		Not changed (N = 2), 50%
Never enters the forest (N = 51), 33.12%	Utilize (N = 11), 21.569%	Changed (N = 11), 100%
		Not changed (N = 0), 0%
	Not utilize (N = 40), 78.431%	Changed (N = 38), 95%
		Not changed (N = 2), 5%

N is the number of samples. % represents the level of behavioral change from the previous condition. * Result inconsistent with the project’s guidelines. *** Result consistent with the project’s guidelines.

.

According to the data presented in Table 6, among the farmers who benefited from the project, two subgroups exhibited original behaviors that may continue to negatively impact the environment: those who “enter the forest 1–3 times per year” and those who “enter the forest frequently.” Together, these groups account for 66.88% of the beneficiaries. When considering only those who utilized the project, it was found that individuals with lower forest access had a higher tendency to shift toward pro-environmental behaviors in line with the project’s objectives (71.43%) compared to those with more frequent forest access (66.67%). This behavioral shift was statistically significant.

Conversely, 33.12% of the project beneficiaries reported never entering the forest, representing an environmentally non-targeted group within the project. Meanwhile, those who entered the forest 1–3 times per year (N6) and those who entered frequently (N1), despite receiving benefits from the project, continued their previous behaviors. These groups can be considered environmentally misaligned targets, as their actions remain inconsistent with the project’s objectives. However, the environmental impact caused by these groups is relatively limited in proportion to the total population.

From an economic perspective, among the 154 sampled respondents, a substantial proportion 119 individuals, accounting for 77.27% did not utilize the project’s benefits. This figure is also relatively small compared to the total community population of 2388 residents. These findings suggest that the project remains far from achieving significant behavioral change in terms of environmental practices and rural economic development, even when considering only those who directly benefited (N184) from the intervention.

4.2.2. The Factors That Impact Economic Development Aimed at Solving the Environmental Problems of the Project

The study defines EV (Environmental problem solving) as pro-environmental behavior, specifically ‘the ability to stop harvesting forest products.’ The analysis employs the CIM to determine behavioral determinants and uses Binary Logistic Regression for statistical testing. The results of the analysis are presented in

Table 7. The analysis results using Binary Logistic Regression.

Variables	β	T Std. Error	Sig.
(V) individual’s valuation	0.132	0.589	0.824
(pi) perceived personal influence	1.683	0.502	0.001 **
(pg) probability the group will succeed	1.845	0.880	0.038 *
(B) selective benefits	3.995	0.937	3.63 × 10−5 ***
(C) Income	−0.074	0.091	0.419
(C) Age	2.022	0.425	4.68 × 10−6 ***
(C) Education	0.425	0.202	0.0369 *
(C) Knowledge about the impact of the problem	1.104	0.499	0.028

* 0.05; ** 0.01; *** <0.001.

.

The variables significantly influence the decision able to stop harvesting the forest products related to the perceived personal influence (p_i) that the sample group has on the environment. Regarding the probability the group will succeed (p_g), which relates to Government efficacy and Citizen efficacy in addressing environmental problems, there is a certain level of significance. When considering the value placed on economics versus the environment from the selective benefits (B) variable, it is highly significant. Finally, Selective Cost (C) in factors related to social capital shows that age is highly significant, while Education is significant to a certain extent.

In contrast, the expected predictive factors under the individual’s valuation component (V) did not show statistical significance. Although the sampled population resided in one of the most severely affected areas by PM2.5 from forest fires in Thailand, environmental risks were not perceived as significant. This finding aligns with the contextual factor (C), particularly in terms of knowledge about the impact of the problem and income, as the sample demonstrated limited awareness. In essence, PM2.5 from forest fires appears to be normalized within the local context and perceived as an ordinary occurrence by the population.

5. Discussion

5.1. Interpretation of SRR and Behavioral Change

Based on the SROI evaluation, the Groundwater Development Project in Ko, Li District, Lamphun Province generated social and economic value through agricultural development and access to clean water in rural areas. However, it did not demonstrate sufficient cost-effectiveness when considering the SRR over a five-year timeframe. This finding aligns with the evaluation of project success in addressing PM2.5 from forest fires using a decision tree, which revealed that although economic development significantly influenced behavioral change among beneficiaries, the scale of change was not substantial enough to create tangible environmental outcomes across the broader population. Therefore, the effectiveness of economic development in mitigating environmental problems depends on the equitable distribution and magnitude of benefits sufficient to offset environmentally damaging behaviors.

Several factors explain the insufficient SRR of the project. First, economic development in developing countries and rural areas is inherently tied to agricultural dependence [46,47]. In this context, utilizing natural resources particularly groundwater and soil becomes necessary to offset income losses from reduced environmentally harmful practices. This necessity contrasts with the normative assumption that natural resource use is inversely related to income. In reality, economic production and development require natural resource inputs, often leading to environmental degradation [48].

Second, rural investment in public goods and infrastructure, such as irrigation systems, forms the basis for long-term rural development [49]. While immediate benefits may be difficult to quantify, such investments are essential for addressing poverty and inequality, despite being implemented in low-potential areas [50]. Facilitating access to higher-paying non-agricultural labor markets is also crucial for enabling sustainable economic transitions without exacerbating poverty [51].

Third, agricultural markets in developing countries are closely tied to macroeconomic systems, resulting in persistently low prices for agricultural commodities [52,53]. As a result, financial proxies derived from market prices tend to undervalue the impact of improved agricultural practices, even when farmers invest in crop diversification or infrastructure to increase productivity.

Fourth, behavioral analysis based on the CIM revealed that behavioral change is primarily driven by personal pro-environmental tendencies, perceived group efficacy, civic responsibility, economic incentives, higher education levels, and increased age, consistent with prior studies [29,41,42]. However, contrary to expectations, environmental risk perception, knowledge about environmental impacts, and income did not significantly influence behavior. This may reflect contextual differences in rural areas where PM2.5 and forest fires are perceived as normalized phenomena.

5.2. Sustainability Perspective

In terms of sustainable development, the concept is often divided into three dimensions: economic, social, and environmental [54,55]. The balanced integration of these dimensions without creating negative impacts in the future remains a major challenge in project implementation. In practice, however, development inevitably requires the use of natural resources. Although the project seeks to address forest fires and PM2.5 by increasing agricultural income to encourage more pro-environmental behavior, it simultaneously relies on natural resources such as groundwater for irrigation, greater use of soil resources, and generates agricultural waste. Moreover, it requires strong contributions from the social dimension, including cooperation, shared values, and social determinants such as age and education.

Investment in projects guided by the principle of sustainable development therefore requires careful consideration of trade-offs across all three dimensions. Achieving sustainability depends on balancing cost-effectiveness and effectiveness in resource use, where maximizing value from natural resources must be weighed against the risks of environmental degradation caused by excessive exploitation.

5.3. Policy Implications and Practical Solutions

Enhancing the social return on investment from groundwater development projects for agriculture primarily depends on two factors related to income generation. The first factor is increasing production through more efficient groundwater distribution, ensuring adequate and appropriate water use to achieve quality yields consistent with the available agricultural labor in the area. The second factor concerns crop selection, which should prioritize high-value, diverse, and locally suitable crops. Both factors must be pursued under the condition of minimizing environmental impacts. For example, cultivating horticultural crops tends to cause less soil degradation, allows better water management, and generates significantly less agricultural waste compared with field crops.

In addition, addressing the PM2.5 problem from forest fires caused by poverty requires a combination of long-term economic infrastructure investment and social measures. While economic development interventions remain necessary, they must be complemented by government-led social measures such as restricting or prohibiting forest product harvesting during the off-season, promoting shared community values, and fostering collective action in addressing environmental challenges. This approach aligns with numerous studies emphasizing the importance of biomass-burning control, raising public awareness [56], applying economic incentives, enforcing legal measures, and implementing behavioral-change strategies among farmers [57]. However, current short-term measures being implemented are not yet enforced with sufficient rigor to effectively reduce PM2.5 emissions from forest fires.

6. Conclusions

The evaluation of the Groundwater Development Project in Ko Subdistrict, Li District, Lamphun Province reveals that economic development alone was not sufficiently effective, as evidenced by the limited impact value and suboptimal SRR, in achieving both economic success and resolving environmental issues in rural areas. Although statistically significant behavioral changes were observed, the magnitude of these changes was inadequate to generate tangible environmental improvements, particularly in terms of reducing PM2.5 emissions from forest fires. This underscores the critical role of social factors, including the cultivation of pro-environmental behavior, community mobilization, and economic incentives to compensate for environmental trade-offs. Furthermore, additional variables such as age and education were found to influence the degree of environmental concern and individuals’ willingness to engage in environmental problem-solving.

The study faces several limitations. Although the evaluation took place post-project implementation, it may still be too early to observe the full impacts of large-scale infrastructure investments. Nevertheless, the findings provide timely insights into areas requiring improvement for future cost-effectiveness. Moreover, SROI calculations are constrained by limitations in valuation techniques and financial proxy data availability. For instance, using defensive expenditure as a proxy for forest fires prevention yields significantly lower values compared to the actual health and livelihood costs associated with PM2.5 exposure.

Future research should focus on enhancing both the economic efficiency and social mechanisms, particularly government-community collaboration, to foster collective action for environmental protection. If successful, value-based impact assessments could more accurately capture long-term benefits and offer a viable solution to Thailand’s persistent PM2.5 from forest fires problem across multiple regions.