Beyond Forests: A Strategic Framework for Climate-Positive Development from Thailand’s Net-Negative Provinces
Sate Sampattagul, Shabbir H. Gheewala, Ratchayuda Kongboon
Sustainability · 2026
As the global climate discourse shifts from mitigation to achieving net-negative emissions, there is a critical need for replicable, real-world models of climate-positive development at a regional scale, particularly in the Global South. This study addresses this gap by conducting a detailed greenhouse gas (GHG) inventory of four diverse provinces in Thailand and analyzing the results through the newly proposed Climate-Positive Pathways Framework (CPPF). Our findings reveal that all four provinces function as significant net-negative GHG sinks. They achieve this status through three distinct archetypes: a Conservation-Dependent pathway, an Agricultural Frontier pathway, and a novel Agro-Sink pathway. Most significantly, in the Agro-Sink model, we find that in specific economic contexts, managed agricultural landscapes can surpass natural forests as the primary driver of regional carbon removal. This typology provides a new, landscape-scale paradigm for cleaner production, proposing these three archetypes as transferable, evidence-based models for regional policymakers. This underscores that effective climate action requires context-specific regional planning that strategically leverages both natural and agricultural capital.
Zero-Burning Strategies for PM2.5 and GHG Mitigation: A Spatial-Temporal Assessment of Crop Residue Burning in Northern Thailand
Sate Sampattagul, Phakphum Paluang, Hisam Samae, Keng-Tung Wu, Shabbir H. Gheewala, Ratchayuda Kongboon
Land · 2026
Agricultural crop residue burning is a major driver of seasonal PM2.5 pollution and greenhouse gas (GHG) emissions in Northern Thailand. This study quantified GHG emissions from the open burning of rice, maize, and sugarcane residues across six provinces (Chiang Mai, Mae Hong Son, Lampang, Uttaradit, Nakhon Sawan, and Kamphaeng Phet) from 2019 to 2024 using the 2006 IPCC emission methodology. Spatiotemporal patterns of fire hotspots were characterized using MODIS and VIIRS satellite data, combined with kernel density estimation (KDE) and land-use classification in ArcGIS Pro. Total non-CO2 GHG emissions (CH4 and N2O, expressed as CO2-eq using GWP100 from IPCC AR5) over the six years totaled 2,599,551 tCO2-eq, with major rice contributing the largest share (35%), followed by sugarcane (24%), second rice (21%), and maize (20%). Nakhon Sawan was the leading emitter (41%), reflecting its extensive rice and sugarcane cultivation. Pearson correlation analysis revealed consistently positive relationships between daily fire hotspot counts and PM2.5 concentrations (r = 0.30–0.84), with the strongest correlations observed in Mae Hong Son, where basin topography traps pollutants. Time-series analysis confirmed pronounced seasonal PM2.5 peaks that exceeded Thailand’s 24-h NAAQS limit (37.5 μg/m3) by 7–9 times in severe years. Biochar production via pyrolysis was evaluated as a zero-burning alternative, with an estimated annual carbon sequestration potential of 2.3–3.5 million tCO2-eq, substantially exceeding emissions from open burning. These findings indicate that crop-residue valorization options—including biochar production, composting, and biochar co-compost—could theoretically offset agricultural GHG emissions and reduce field-burning PM2.5 emissions in Northern Thailand. However, the realized mitigation will depend on (i) verification of biochar long-term stability in tropical Thai soils through dedicated in situ trials, (ii) economic incentives that offset biochar production costs of approximately 1500–3500 THB per tonne, and (iii) integration within a policy mix that combines burning bans, mechanization support, and farmer extension services. Without these enabling conditions, biochar should be regarded as a future-perspective option rather than an immediately deployable solution.
Carbon Footprint Data Flow Process Improvement for Strawberry Jam Tube Product by Lean Techniques
Kritiya Kanjina, Sakgasem Ramingwong, Nivit Charoenchai, Jutamat Jintana, Sate Sampattagul
Sustainability · 2026
Environmental transparency in food manufacturing requires efficient carbon footprint data collection, yet multi-departmental coordination often creates time-consuming, fragmented processes that impede adoption. This study applies lean office methodologies to optimize carbon footprint assessment processes in food manufacturing. Using a case study approach at a Thai food processing facility, we implemented flow process charts, value stream mapping, eight waste analysis, and ECRS methodology to evaluate the data collection process for strawberry jam production. The baseline assessment documented 142 activities across 12 departments, requiring 17,540 min. The lean interventions included establishing a centralized cross-functional team, developing standardized data collection templates, implementing a unified digital repository system, and consolidating redundant verification procedures. The improved process reduced activities from 142 to 63, decreased the required time from 17,540 to 11,190 min (36.2% reduction), and eliminated 95.8% of non-value-added activities while maintaining regulatory compliance. These efficiency gains enable more frequent environmental assessments and facilitate the broader adoption of carbon footprint measurement within resource-constrained manufacturing contexts. The study demonstrates that lean principles effectively optimize environmental assessment processes themselves, providing a replicable framework adaptable across diverse food manufacturing facilities and product lines while addressing critical adoption barriers including resource constraints and administrative complexity.
Treatment costs and determinants in PM2.5-attributable respiratory diseases: evidence from Northern Thailand
Pheerasak Assavanopakun, Kannika Jarernwong, Sate Sampattagul, Jinjuta Panumasvivat
BMC Public Health · 2026
BACKGROUND: Environmental issues related to air pollution in Southeast Asia have persisted for more than a decade, especially in Thailand. This study aims to estimate the treatment costs of respiratory diseases caused by exposure to ambient PM₂.₅ and to identify the factors that influence these costs. METHODS: This retrospective study analyzed secondary data on OPD and IPD respiratory disease treatment costs from government hospitals, along with ambient PM₂.₅ data from low-cost monitoring stations, to estimate the cost of illness across 25 districts in Chiang Mai during Thailand's fiscal year 2023. Economic cost was estimated using the Cost-of-Illness method formula: Economic Cost Loss = Health Impact × Treatment Cost. K-means cluster analysis was used to classify estimated costs into minimum, medium, and maximum cost scenarios. Multiple linear regression was applied to identify significantly associated factors with treatment cost. RESULTS: Under the maximum cost scenario identified through K-means cluster analysis stratification, the total treatment cost associated with an average PM₂.₅ concentration of 42.59 µg/m³ was 460,122.58 USD, averaging 41.62 USD per case. Each 1 µg/m³ increase in PM2.5 was associated with a cost rise ranging from 403.84 to 13,159.87 USD. Non-infectious respiratory diseases incurred costs approximately two times higher than infectious ones. The estimate of maximum treatment burden for respiratory disease cases was highest in urban areas, totaling 102,878.88 USD. The urban area showed a significantly higher cost of treatment both in OPD and IPD cases (p < 0.001). Moreover, higher healthcare levels and older age were associated with higher costs in OPD cases. In IPD cases, length of hospital stay was a significant predictor. CONCLUSIONS: Ambient PM₂.₅ exposure contributes significantly to the economic burden of respiratory diseases in polluted areas. These highlight the importance of pollution control policies and healthcare resource planning in high-risk areas. TRIAL REGISTRATION: not applicable.
Precision and Accuracy Analysis of PM2.5 Light-Scattering Sensor: Field and Laboratory Experiments
Hisam Samae, Phuchiwan Suriyawong, Artit Yawootti, Worradorn Phairuang, Sate Sampattagul
Atmosphere · 2025
The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with the established standards set by the US Environmental Protection Agency (US EPA). This study evaluates the accuracy and reliability of the DustBoy, a commonly used PM2.5 monitoring device in Thailand. A comparative analysis was conducted between the DustBoy and the US EPA’s Federal Reference Method (FRM) and Federal Equivalent Method (FEM). The research involved both laboratory and field testing, where the DustBoy’s performance was analyzed at various particulate matter concentration levels and environmental conditions. The study demonstrated that the DustBoy readings diverged from those of standard monitors at higher PM2.5 concentrations; however, a positive correlation between the devices remained evident. Below 100 µg/m3, the DustBoy overestimated PM concentrations compared to the FRM devices but underestimated them compared to the FEM devices. At higher concentrations, the DustBoy showed a significant overestimation, although the data trends aligned with those of standard devices. The sensor performance was also affected by factors such as the sensor age and device model. Corrections were developed to adjust the DustBoy readings to match the reference devices more closely, enhancing the accuracy post-adjustment. These corrections will refine the DustBoy’s public data reporting and serve as guidelines for other low-cost sensors in Thailand.
Optimizing Residential Energy Usage with Smart Devices: A Case Study on Energy Efficiency and Environmental Sustainability
Nat Weerawan, Phuchiwan Suriyawong, Hisam Samae, Sate Sampattagul, Worradorn Phairuang
Sustainability · 2025
In this study, we examined the impact of an intelligent system and air conditioning control on power consumption. The experiment was carried out during five distinct time periods: (1) background room usage, (2) smart system setup, (3) air conditioning control to maintain room temperature at no more than 27 °C, (4) air conditioning temperature control during working hours, and (5) air conditioning operated continuously to maintain the room temperature at 27 °C. For each time period, the daily power consumption was evaluated, and outliers were identified and eliminated using a threshold derived from the hourly average. The findings demonstrated that the smart system setup period and air conditioning control resulted in lower usage compared to continuously operated air conditioning with substantial spikes in demand. The impacts of the novel system and air conditioning control on energy consumption were revealed through statistical analysis, which included regression models and hypothesis tests. According to this study’s findings, it is essential to regulate spikes and guarantee proper operation to reduce the carbon footprint while maintaining a comfortable atmosphere. Notably, the integration of the smart system and optimized scheduling resulted in a substantial decrease in greenhouse gas emissions, with annual carbon emissions reduced by up to 65% compared to continuously operated air conditioning without smart control. Moreover, these systems can optimize energy use.
Economic Viability of Electric Bus Adoption for Public Transportation in Thailand: A Monte Carlo Simulation Approach
Sakgasem Ramingwong, Sate Sampattagul, Jutamat Jintana
Logistics · 2025
Background: Thailand is actively transitioning toward electric vehicle adoption as part of its commitment to reducing greenhouse gas emissions. This study investigates the economic feasibility of replacing diesel buses with electric buses in Thailand’s public transportation sector. Methods: The research employs a comprehensive methodological framework combining Total Cost of Ownership (TCO) analysis with Monte Carlo simulation to address uncertainties in long-term financial projections. The study examines four pilot routes operated by a major Thai bus company, incorporating potential carbon credit revenues through Thailand’s Voluntary Emission Reduction program. Results: The analysis reveals substantial cost advantages for electric buses across all examined routes, with TCO savings ranging from 23.07% to 38.25%. Even under conservative scenarios, all routes demonstrate positive economic benefits. The fleet-wide net savings amount to approximately 236 million THB over a 10-year period, with an additional 16.7 million THB potential carbon credit revenue. Sensitivity analysis identifies fuel costs as the most significant factor (45.2%) affecting economic outcomes. Conclusions: The transition to electric buses presents a compelling economic and environmental case for Thai public transportation operators, with significant cost savings and emission reductions. A phased implementation approach beginning with routes offering the highest percentage savings is recommended.
A Blueprint for Data-Driven Climate Action: A Quantified Mitigation Pathway for Chiang Mai Using GHG Accounting and Spatial Analysis
Sate Sampattagul, Phakphum Paluang, Shabbir H. Gheewala, Ratchayuda Kongboon
Urban Science · 2025
This study develops a replicable, data-driven framework for subnational climate action, demonstrated through a case study of Chiang Mai Province, Thailand. The framework integrates a comprehensive greenhouse gas (GHG) inventory with spatial analysis to identify and quantify location-specific mitigation strategies. Using 2019 as the base year, total emissions were 5,387,482 tCO2e (BASIC+), dominated by stationary energy (40%) and transportation (32%). Under a Business-as-Usual scenario, emissions are projected to reach 6.35 million tCO2e by 2030, highlighting an urgent need for intervention. As a key mitigation strategy, this research conducts a detailed spatial analysis of solar rooftop potential. The findings reveal a significant opportunity: a conservative 30% adoption rate on suitable rooftops could generate approximately 2070 GWh of clean energy annually, leading to an emissions reduction of over 1 million tCO2e. Crucially, this single intervention could offset 16% of the province’s projected 2030 emissions. This study presents a viable pathway for subnational entities to contribute to national climate targets, offering a practical blueprint for other cities and regions globally to develop effective, evidence-based climate action plans.
Health Impact Related to Ambient Particulate Matter Exposure as a Spatial Health Risk Map Case Study in Chiang Mai, Thailand
Kannika Jarernwong, Shabbir H. Gheewala, Sate Sampattagul
Atmosphere · 2023
Chiang Mai has been one of the most polluted cities globally, exceeding the PM2.5 quality standards for decades and facing hazardous air pollution on an annual basis. As ambient PM2.5 strongly affects human health, this study aims to investigate the hotspots of PM2.5 and health impact areas due to exposure to PM2.5 by illustrating a spatial distribution via a Chiang Mai health risk map. The association between PM2.5 concentration and human health impact were assessed using Pearson’s correlation, focused on the peak period from January to April 2021 in Chiang Mai. The primary data on PM2.5 concentration were collected using low-cost sensors. The health impact is based on the number of hospital admissions in all incidences of diseases due to PM2.5 exposure following the ICD-10. The results showed that the highest polluted and health-risk areas were located in the center of Chiang Mai, especially in the Mueang district. PM2.5 concentration was highly correlated with the incidence of dermatitis (R = 0.84), conjunctivitis (R = 0.81), stroke (R = 0.74), and lung cancer (R = 0.73). Thus, the increased PM2.5 concentration resulted in heightened hospital admissions. The results provide insightful information for policymakers and local public health organizations regarding priority areas in resource management.
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