Annex A. ENV-Linkages model
The OECD ENV-Linkages model is a dynamic multi-sectoral, multi-regional CGE model that links economic activities to energy and environmental issues. A more comprehensive model description is given in Chateau et al. (2014[1]). While ENV-Linkages can provide emission projections for greenhouse gases and air pollutants, for this report, emissions of air pollutants are provided by the GAINS model, based on ENV-Linkages’ economic projections. Indeed, GAINS can provide a higher regional disaggregation with country-specific emission projections for all Arctic Council countries.
Production in ENV-Linkages is assumed to operate under cost minimisation with perfect markets and constant return to scale technology. The production technology is specified as nested constant elasticity of substitution (CES) production functions in a branching hierarchy. This structure is replicated for each output, while the parameterisation of the CES functions may differ across sectors. The nesting of the production function for the agricultural sectors is further re-arranged to reflect substitution between intensification (e.g. more fertiliser use) and extensification (more land use) of crop production, or between intensive and extensive livestock production. The structure of electricity production assumes that a representative electricity producer maximises its profit by using the different available technologies to generate electricity using a CES specification with a large degree of substitution. The structure of non-fossil electricity technologies is similar to that of other sectors, except for a top nest combining a sector-specific resource with a sub-nest of all other inputs. This specification acts as a capacity constraint on the supply of the electricity technologies.
Energy is a composite of fossil fuels and electricity. In turn, fossil fuel is a composite of coal and a bundle of the “other fossil fuels”. At the lowest nest, the composite “other fossil fuels” commodity consists of crude oil, refined oil products and natural gas. The values of the substitution elasticities are chosen as to imply a higher degree of substitution among the other fuels than with electricity and coal.
The model adopts a putty/semi-putty technology specification, where substitution possibilities among factors are assumed higher with new vintage capital than with old vintage capital. In the short run, this ensures inertia in the economic system, with limited possibilities to substitute away from more expensive inputs. However, in the longer run, this implies relatively smooth adjustment of quantities to price changes. Capital accumulation is modelled as in the traditional Solow/Swan neo-classical growth model.
Household consumption demand is the result of static maximisation behaviour, which is formally implemented as an “extended linear expenditure system”. A representative consumer in each region – who takes prices as given – optimally allocates disposal income among the full set of consumption commodities and savings. Savings are considered as a standard good in the utility function and do not rely on forward-looking behaviour by the consumer. The government in each region collects various taxes to finance government expenditures. Assuming fixed public savings (or deficits), the government budget is balanced through the adjustment of the income tax on consumer income. In each period, investment net-of-economic depreciation is equal to the sum of government savings, consumer savings and net capital flows from abroad.
International trade is based on a set of regional bilateral flows. The model adopts the Armington specification, assuming that domestic and imported products are not perfectly substitutable. Moreover, total imports are also imperfectly substitutable between regions of origin. Allocation of trade between partners then responds to relative prices at the equilibrium.
Market goods equilibria imply that, on the one side, the total production of any good or service is equal to the demand addressed to domestic producers plus exports; and, on the other side, the total demand is allocated between the demands (both final and intermediary) addressed to domestic producers and the import demand.
ENV-Linkages is fully homogeneous in prices and only relative prices matter. All prices are expressed relative to the numéraire of the price system that is arbitrarily chosen as the index of OECD manufacturing exports prices. Each region runs a current account balance, which is fixed in terms of the numéraire. One important implication from this assumption in the context of this report is that real exchange rates immediately adjust to restore current account balance when countries start exporting/importing emission permits.
As ENV-Linkages is a recursive-dynamic model and does not incorporate forward-looking behaviours, price-induced changes in innovation patterns are not represented in the model. However, the model does entail technological progress through an annual adjustment of the various productivity parameters in the model, including autonomous energy efficiency and labour productivity improvements. Furthermore, as production with new capital has a relatively large degree of flexibility in choice of inputs, existing technologies can diffuse to other firms. Thus, within the CGE framework, firms choose the least-cost combination of inputs, given the existing state of technology. The capital vintage structure also ensures that such flexibilities are larger in the long run than in the short run.
The sectoral and regional aggregation of the model, as used in the analysis for this report, are given in Tables A.1 and A.2, respectively.
The baseline economic trends are described in the recent Global Material Resources Outlook to 2060 (OECD, 2019[2]). For the dynamic calibration of ENV-Linkages to 2050, macroeconomic projections are based on two long-run macroeconomic growth models. First, the growth scenarios result from simulations of the OECD Economics Department (Guillemette and Turner, 2018[3]). These projections cover 42 OECD and G20 countries up to 2060. Second, the ENV-Growth model, hosted at the OECD Environment Directorate, is used to complete these projections for countries not covered by the OECD’s Economic Department. Together, macroeconomic projections are provided for almost 180 countries.
The baseline construction also reproduces specific sectoral trends for the energy and agricultural sectors. Energy system projections are calibrated to the 2018 World Energy Outlook (IEA, 2018[4]) and they are fundamental to ensure that energy-related emissions reflect the latest energy trends.
The economic feedbacks of air pollution are modelled directly in ENV-Linkages following a production function approach, as outlined in The Economic Consequence of Outdoor Air Pollution (OECD, 2016[5]). This means that market impacts directly affect specific elements in the economic system, such as labour productivity or land productivity. The impacts are thus modelled as changes in the most relevant parameters of the production function underlying the model structure.
Changes in health expenditures are implemented in the model as a change in demand for health services (in the model part of the aggregate non-commercial services sector). These health expenditures reflect costs related to treatments of the illnesses as well as hospital admissions. The additional health expenditures affect both households and government expenditures on healthcare. The distinction between households and government expenditures is based on World Bank data on the proportion of healthcare expenditures paid by households and by the government (World Bank, 2015[6]). Health expenditures caused by outdoor air pollution are calculated multiplying the number of cases for each illness (e.g. chronic bronchitis) with a corresponding unit cost value (e.g. the health expenditures linked to a case of chronic bronchitis), using a methodology similar to the cost of illness approach in which only the tangible healthcare costs are considered. The reference unit values for the healthcare costs used in this report for the OECD, which are outlined in Table A.3, are established based on existing studies, as elaborated in Holland (2014[7]). These representative OECD values are then adapted to individual countries, multiplying them by the ratio of each country’s income and the average OECD income, for each year.
Changes in labour productivity are directly implemented in the model as percentage changes in the regional productivity of the labour force. Productivity losses are calculated from lost work days, following the methodology used in Vrontisi et al. (2016[8]). This methodology calculates labour productivity losses as proportional to the number of lost work days, as compared to the average number of work days per year in each region (World Bank, 2014[9]).
Changes in crop yields are implemented in the model as a combination of changes in the productivity of the land resource in agricultural production, and changes in the total factor productivity of the agricultural sectors. This specification, which is in line with OECD (2015[10]), mimics the idea that agricultural impacts affect not only purely biophysical crop growth rates but also other factors such as management practices. Air pollution affects crop yields heterogeneously in different world regions, depending on the concentrations of ground-level ozone. Overall, the demand for agricultural products, which changes over time in the model even in the baseline scenario, is affected in each region by the air pollution-driven changes in crop yields.
Once the shocks from the air pollution impacts are incorporated in ENV-Linkages, the model finds a new equilibrium that takes into account the impacts of air pollution. Following the adjustment processes that takes place in the model, the direct impacts of air pollution also result in indirect impacts. For instance, an increased demand for healthcare may result in a lower demand for other services, while changes in crop yields for certain crops may result in changes in production of substitute crops or related economic activities (such as food production). These changes in production can then lead to changes in trade patterns.