In recent decades, the carbon sink provided by the U.S. forest sector has offset a sizable portion of domestic greenhouse gas (GHG) emissions. In the future, the magnitude of this sink has important implications not only for projected U.S. net GHG emissions under a reference case but also for the cost of achieving a given mitigation target. The larger the contribution of the forest sector towards reducing net GHG emissions, the less mitigation is needed from other sectors. Conversely, if the forest sector begins to contribute a smaller sink, or even becomes a net source, mitigation requirements from other sectors may need to become more stringent and costlier to achieve economy wide emissions targets. There is acknowledged uncertainty in estimates of the carbon sink provided by the U.S. forest sector, attributable to large ranges in the projections of, among other things, future economic conditions, population growth, policy implementation, and technological advancement. We examined these drivers in the context of an economic model of the agricultural and forestry sectors, to demonstrate the importance of cross-sector interactions on projections of emissions and carbon sequestration. Using this model, we compared detailed scenarios that differ in their assumptions of demand for agriculture and forestry products, trade, rates of (sub)urbanization, and limits on timber harvest on protected lands. We found that a scenario assuming higher demand and more trade for forest products resulted in increased forest growth and larger net GHG sequestration, while a scenario featuring higher agricultural demand, ceteris paribus led to forest land conversion and increased anthropogenic emissions. Importantly, when high demand scenarios are implemented conjunctively, agricultural sector emissions under a high income-growth world with increased livestock-product demand are fully displaced by substantial GHG sequestration from the forest sector with increased forest product demand. This finding highlights the potential limitations of single-sector modeling approaches that ignore important interaction effects between sectors.
Structural economic optimization models of the forestry and land use sectors can be used to develop baseline projections of future forest carbon stocks and annual fluxes, which inform policy dialog and investment in programs that maintain or enhance forest carbon stocks. Such analyses vary in terms of the degree of spatial, temporal, and activity-level aggregation used to represent forest resources, land cover, and markets. While the statistical and econometric modeling communities widely discuss the effects of aggregation bias and have developed correction techniques, there is limited prior research investigating how aggregation bias may affect structural optimization models. This paper explores potential aggregation bias using the Land Use and Resource Allocation model (LURA), a detailed spatial allocation partial equilibrium model of the U.S. forest sector. We ran a series of projections representing alternative aggregation approaches including averaging forest stocks at plot, county, state, and regional levels, across one-, five, or ten-year age classes, and by two or fourteen forest types. We compared the resulting projections of forest carbon stocks and harvesting activities across each aggregation scenario. This allows us to isolate the effect of aggregation on key variables of interest (e.g., GHG emissions and supply costs), while holding all other structural characteristics of the modeling framework constant. We find that age-class and forest type aggregations have the greatest impact on modeling results, with the potential to substantially impact market and greenhouse gas projections. On the other hand, spatial aggregation has a small impact on national carbon stock projections. Importantly, regional results are greatly impacted by different aggregation approaches, with projected regional cumulative carbon stocks differing by more than 25% across scenarios.
Using Ireland as a case study, this study examines the economic drivers of the farm afforestation decision for individual farms. Farm incomes and characteristics are observed across the distribution of livestock farmers, using a longitudinal dataset. Potential agricultural and forest income streams are generated and compared in a life-cycle theoretical framework, while the inclusion of attitudinal survey data in the analysis is shown to contribute significantly to the understanding of the planting decision. The study suggests that there is a cohort of younger farmers on larger holdings who might plant if potential forest income is greater than their agricultural income, but we also find that there is a cohort of older farmers on smaller holdings that will never plant, and for whom negative cultural attitudes are stronger than economic drivers. The study concludes that a ‘one size fits all’ programme based solely on financial incentives may not be the most appropriate means to encourage further farm afforestation and suggests that more targeted approaches may be necessary to nuance incentives to increase afforestation rates and facilitate the use of afforestation as an agricultural greenhouse gas mitigation mechanism.
A model of two regions with a common wood market is introduced. Regions may be of two types, according to their forest management regime, namely managed forest plantations (M) and unmanaged open access forests (U). It is found that when regions are of the same type, unilateral climate policy in the forestry sector leads to (positive) carbon leakage. However, when regions are of different types, unilateral climate policy results in negative carbon leakage. Thus, policies aimed at increasing diversity in management regimes, within a wood market, stimulate the emergence of market forces that preserve and enhance forest carbon.
The sequestration of CO2 in forests is often suggested as a means to offset greenhouse gas emissions. New Zealand’s experience suggests the effects of government programmes to provide carbon credits to forest owners could be enhanced if forward markets, futures markets, or carbon-lending markets were used to manage risks. This paper provides a comparative institutions approach based on the history of commodity markets to argue that carbon lending markets, not forward or futures markets, are likely to be the most convenient form of a forestry carbon market. A carbon lending market will raise the total returns from forestry investments with minimal risks to forest owners, and simultaneously reduce the risks facing other firms contemplating carbon reducing investments. For this reason, governments wishing to include forest sequestration in an Emissions Trading Scheme may wish to encourage the development of a carbon lending market.
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