Cost-effective Policies to Control Diffuse Agricultural Externalities in a Palm Oil and Rubber-dominated Tropical Catchment

Abstract

This study develops a nonlinear biophysical economic model to identify cost-effective policies to control diffuse agricultural pollution externalities in a tropical Malaysian catchment dominated by palm oil and rubber plantations in a 30-year cycle. While these plantations are vital to the economy, they generate significant pollutants, including nitrogen (N), potassium (K), sediment (Z), and greenhouse gases (GHG). The model captures complex catchment processes utilising 30 years of weather data and incorporates significant farm-level heterogeneity to assess policies' environmental and economic impacts.

In addition to input taxes on N and K, we simulated novel instruments suited to developing tropical countries that 1) spatially target (ST) the plantation of commercial forestry (CF) and natural forestry (NF) to the most polluting soils and/or allocate the rubber production to the least polluted soil, 2) reallocate existing palm oil and rubber subsidies on government managed farms to CF, SRI (palm oil) and SRI (rubber) respectively), 3) impose restrictions on N and K applications on government managed palm oil producing farms (ECC), and 4) hybrid combinations of the above instruments. These were simulated to leverage existing relatively low-cost ST command and control policies. Three regulatory target (RT) levels equating to a 20%, 40% and 60% reduction in pollutant levels from the baseline are considered. The study reveals that specific policy instruments perform better for different pollutants. For N pollution, combining SRI (palm oil) + nitrogen input tax (NIT) was the most cost-effective, while K pollution was managed best with SRI (rubber). Z pollution was mitigated by targeting rubber production on clay soils and CF on sandy and silty soils.

Additionally, SRI (palm oil) + NIT emerged as the most cost-effective GHG reduction strategy. At the 60% RT level, SRI (palm oil) + NIT was optimal across all pollutants. SRI policies also performed well at lower RTs, and cost-effectively controlled various pollutant combinations. Given the economic and political challenges of controlling diffuse pollution in developing countries, the research also simulates a social cost targeting approach, which seeks to deliver the most pollution reduction across pollutants for a politically acceptable social cost.