State simplification, heterogeneous causes of vegetation fires and implications on local haze management: case study in Thailand

The article studies the nature of current policy and management practice in Thailand on vegetation burnings and haze pollution and examines how these take into account heterogeneity and complexity of local conditions and causal factors on the ground. Chang Mai province’s recent haze episodes are used as case study. The country’s policy regime on vegetation burning and haze pollution is characterized as command-and-control and highly regulatory. Authors argue that haze problem is framed by the central and provincial government as a purely an administrative task of control and penalizing perpetrators of fire. This fails to take into account the heterogeneity and complexity of local conditions and drivers of burning occurrences, which in Chang Mai involves a variety of widely diffused forest and farming-based livelihood activities, such as hunting and forest product gathering, swidden farming, and burning of agricultural residues in rice cultivation. This state simplification in policy combines well with and is further reinforced by a centralized, top-down, and institutional landscape and functioning of government in decision-making, enabling the policy to cascade down to the province, districts, and sub-districts basically unaltered. Further, traditional administrative separatism between ministries and their provincial counterparts undermines the possibility of area-wide planning and integration of responses. The authors’ recommend a major policy shift, among others, including components of using local research on causal factors as tool for planning and policy, instituting incentives and reward systems for would-be fire igniters, area-wide local-transboundary approach, and strengthening autonomy of local government bodies. Authors have used documents review, secondary sources, and key informant interviews.     

Projecting Land-Use Change and Its Consequences for Biodiversity in Northern Thailand

Rapid deforestation has occurred in northern Thailand over the last few decades and it is expected to continue. The government has implemented conservation policies aimed at maintaining forest cover of 50% or more and promoting agribusiness, forestry, and tourism development in the region. The goal of this paper was to analyze the likely effects of various directions of development on the region. Specific objectives were (1) to forecast land-use change and land-use patterns across the region based on three scenarios, (2) to analyze the consequences for biodiversity, and (3) to identify areas most susceptible to future deforestation and high biodiversity loss. The study combined a dynamic land-use change model (Dyna-CLUE) with a model for biodiversity assessment (GLOBIO3). The Dyna-CLUE model was used to determine the spatial patterns of land-use change for the three scenarios. The methodology developed for the Global Biodiversity Assessment Model framework (GLOBIO 3) was used to estimate biodiversity intactness expressed as the remaining relative mean species abundance (MSA) of the original species relative to their abundance in the primary vegetation. The results revealed that forest cover in 2050 would mainly persist in the west and upper north of the region, which is rugged and not easily accessible. In contrast, the highest deforestation was expected to occur in the lower north. MSA values decreased from 0.52 in 2002 to 0.45, 0.46, and 0.48, respectively, for the three scenarios in 2050. In addition, the estimated area with a high threat to biodiversity (an MSA decrease >0.5) derived from the simulated land-use maps in 2050 was approximately 2.8% of the region for the trend scenario. In contrast, the high-threat areas covered 1.6 and 0.3% of the region for the integrated-management and conservation-oriented scenarios, respectively. Based on the model outcomes, conservation measures were recommended to minimize the impacts of deforestation on biodiversity. The model results indicated that only establishing a fixed percentage of forest was not efficient in conserving biodiversity. Measures aimed at the conservation of locations with high biodiversity values, limited fragmentation, and careful consideration of road expansion in pristine forest areas may be more efficient to achieve biodiversity conservation.     

Applying Gap Analysis and a Comparison Index to Evaluate Protected Areas in Thailand

Protected areas in Thailand were first established 40 years ago. The total area of existing protected areas covers 18.2% of the country’s land area and the Class 1 Watershed, another form of protection, encompasses 18.1%. The government of Thailand intends to increase protected area systems to 25% of the country in 2006 and 30% in 2016. There are always questions arising about how much is enough protected areas to effectively protect biodiversity. The objective of this article is to assess the representation of ecosystems in the protected area network. This article also recommends which underrepresented ecosystems should be added to fill the gaps in representativeness. The research applies a gap analysis and a comparison index to assess the representation of ecosystems within the protected area network. The spatial analyses were applied to measure three aspects of representativeness, namely forest type, altitude, and natural land system. The analyses indicate that the existing protected area system covers 24.4% of the country’s land area, nearly meeting the 25% target proposed by the National Forest Policy; and 83.8% of these areas are under forest cover. Most protected areas are situated in high altitudes, where biological diversity is less than in lowlands. Mangrove forest and riparian floodplain are extremely underrepresented in the existing system. Peat swamp forest, dry dipterocarp forest, and beach forest are relatively well represented. In addition, these five ecosystems are threatened by human pressures and natural disasters; therefore, they should be targeted as high priorities for the selection of new reserves. Future research should incorporate aquatic and marine ecosystems, as well as animal distributions, which were not included in this research due to data unavailabilities.