Assessing future sustainability by forecast of Genuine Savings paths

Abstract

The World Development Indicators (WDI) database provides historical trends of the Genuine Savings (GS) indicator, going back more than 40 years for about 200 countries. The indicator accounts for subsoil mineral depletion and global warming as environmental degradation. The major shortcoming of existing studies is that they have only considered present and past values of GS. This paper describes an approach to provide future paths of GS until the end of the current century by applying an integrated assessment model (IAM). To cover various mineral resources as well as their environmental impacts, we combine an originally developed mineral resource model and an existing detailed environmental impact assessment model with an existing, orthodox IAM. To be consistent with the WDI, we include data for subsoil resource depletion. Moreover, while applying the IAM, we consider a wider coverage of environmental impact categories compared to WDI. Our findings indicate that, after 2030, GS are positive, thus indicating sustainability in many geographical regions, except Latin America. The results also show that the critical factor that pushes down Gross Savings to GS is the environmental external cost from human appropriation of net primary productivity via land use and changes in land use.

Appendices

Appendix 1

1.1 A brief outline of our model

1.1.1 Model framework and inter-linkages among models

The model applied in this study can be regarded as an extension of several models: (1) energy and economy models from an extant IAM (GRAPE (Kurosawa et al. 1999) with major revisions, (2) a life cycle impact assessment model [LIME (Itsubo and Inaba 2005, 2010)], which has been modified to be applicable to 10 regions with 15 time steps, and (3) an originally developed nonfuel mineral resources balance model. The following subsections explain sub models for FC, NFC, LUC, and DC in Eqs. (10) and (15).

1.1.2 Fuel and nonfuel minerals

The submodels of fuel and nonfuel mineral resources [FC and NFC, respectively, in Eq. (10)] are cost models for fuel minerals (fm; oil, gas, coal, and uranium) and nonfuel mineral resources (nfm; iron, bauxite, copper, lead, zinc, and limestone). These are demand and supply models in which supply deals with mining, milling, dressing, smelting, and refining for nfm; the electrical or chemical conversion processes via various detailed energy technologies for fm; transportation among the ten regions, and the final demand for both energy (${{EL}}_{{rg,yr}}$ and ${{NE}}_{{rg,yr}}$) and materials (${{MD}}_{{\sec ,{{nfm}},{{rg}},{{yr}}}}$) of three representative manufacturing sectors (electricity and machinery, construction and building, and motor cycles). The nfm exist as in-use stocks of goods produced during the assumed products’ lifetimes, after which they become out-of-use stocks, and are then finally disposed of or recycled.

1.1.3 Land use and land use change

The land use submodel calculates the endogenous 5 categories of land use [forestry, grassland (pasture land), cropland, urban, and others] and 20 kinds of land use change [LUC in Eq. (10)] among the categories (=5 × 4), by satisfying exogenous demand for foods (pork, chicken, mutton, beef, rice, wheat, and corn), wood (logs, timber/boards, wood pulp, and paper) and area of urban land (i.e., land area requirement for human settlement), using exogenous costs of land rent, land conversion, and food production.

The area of urban land is calculated using population and population density. The land category “other” includes all other categories, such as desert terrain and reservation land, the area of which is kept constant. In short, the land area of “others” is constant, urban area is decided by population and population density, forestry is driven by demands for foods, wood, and global warming constraints, and both grassland and cropland satisfy aggregated foods demand.

1.1.4 External damage costs

${{DC}}_{{rg,yr}}$ is used to calculate the environmental external cost to evaluate the monetary value of environmental degradation for nonmarket goods. DC can be calculated using Eqs. (15) and (16).

The weighting factor ${{WF}}_{{{{sgo}},{{JPN}},{{yr}}_{0} }}$[or marginal willingness to pay (MWTP)] and the DR relation ${{DR}}_{{{{sgo}},{{sbs}},{{rg}},{{yr}}}}$ are available exogenously from LIME. They are related to four endpoints (or safeguard objects) via ${{DR}}_{{{{sgo}},{{sbs}},{{rg}},{{yr}}}}$. Then, the four endpoints are aggregated into a single index (i.e., monetary term) by ${{WF}}_{{{{sgo}},{{JPN}},{{yr}}_{0} }}$ obtained through conjoint analysis. ${{Inv}}_{{{{sbs}},{{rg}},{{yr}}}}$ refers to inventories treated in the model, such as CO₂, SO _x , and NO _x from fuel combustion, CO₂ release via deforestation, 5 kinds of NCGHGs, 14 kinds of ODS, extraction and disposal of nonfuel minerals, and LU&LUC. NCGHGs and ODS are exogenous, while the rest are endogenous.

${{DR}}_{{{{sgo}},{{sbs}},{{rg}},{{yr}}}}$ and ${{WF}}_{{{{sgo}},{{JPN}},{{yr}}_{0} }}$ in LIME are adjusted to be made compatible with all regions and time steps of our model (Kosugi et al. 2009). The DR values in LIME are originally applicable to Japanese circumstances and were adjusted to all regions and time steps to suit the proposed model. The differences in region and time compared to present-day Japan are reflected by using a zero-order approximation that considers the damage and impact to safeguard objects. To be specific, the ratio (between the value in a region in a time step as numerator and the value of the present day as a denominator) is multiplied by DR values in present-day Japan. The fraction of population density (for human health), the ratio of population density for human health per capita GDP for natural resources, potential NPP for NPP, and the extinction risk of vascular plants for biodiversity are considered for the multiplication. ${{WF}}_{{{{sgo}},{{JPN}},{{yr}}_{0} }}$ is transferred to other regions by using benefit transfer [income elasticity σ of 0.5 from Pearce (2003)].

Appendix 2

2.1 Impact categories treated in our model

2.1.1 Global warming

We take the following steps in order to develop damage functions to safeguard the subjects of human health (Itaoka et al. 2002) and natural resources (Uchida et al. 2002). First, damage due to the impact pathway with doubled CO₂ concentration is estimated as a benchmark, while global mean temperature is projected using the DICE model (Nordhaus 1994). Next, time series impacts are estimated by interpolation and extrapolation based on the benchmark impacts considering regional population change (United Nations 2003) and economic development (Nakićenović et al. 1998). Last, the damages are aggregated to estimate impacts for unit emission of GHG.

2.1.1.1 Damages to human health

For thermal/cold stress, a DR coefficient between daily maximum temperature and mortality is expressed as a function of regional GDP per capita and annual average air temperature by applying a Japanese coefficient as the reference coefficient (Honda et al. 1998). For malaria, the population in areas at risk of malaria, with and without climate change, as simulated by Matsuoka and Kai (1995), is used to estimate the rate of population increase at malaria risk per 1 °C temperature rise. The increased rate of dengue risk due to temperature increase is assumed to be double that of malaria, based on Martens et al. (1997). For natural disasters, LIME refers to the expert judgments applied by ExternE (European Commission 1999), which determined that damages by typhoons and other natural disasters would increase by 25 and 10 %, respectively, with a 2.5 °C increase in global mean temperature. The increase in the number of people at risk of hunger due to temperature increase is estimated based on Parry et al. (1999).

2.1.1.2 Damages to natural resources

Future crop production up to the point of doubled CO₂ concentration, excluding the CO₂ fertilization effect, is calculated using the model of potential crop productivity developed by Kyoto University and the National Institute of Environmental Studies, Japan (Takahashi et al. 1997). In addition, the CO₂ fertilization effect is calculated based on Cure and Acock (1986). To estimate the change in energy consumption for heating and cooling resulting from global warming, future heating and cooling degree days are calculated and the interactions among economic growth, heating, and cooling energy consumption are analyzed using empirical energy consumption data for Japan (Energy Data and Modeling Center, Institute of Energy Economics, Japan 2002). The land elevation dataset ETOPO5 accessible via GRID-Tsukuba, originally developed by the NOAA National Geophysical Data Center (NGDC) is used to calculate the areas of submergence in the case of a 0.5 m sea level raise that plausibly corresponds to a doubled CO₂ concentration in 2100.

2.1.2 Land use

The increment in the extinction risk of vascular species and the decrement in NPP of vegetation, as indicators of biodiversity and primary productivity, respectively, are assessed as damage indicators (Nakagawa et al. 2002). These damages are considered to be incurred by land use (land occupation) and land use change (land transformation).

2.1.2.1 Damages to biodiversity

Originally based on the idea of extinction probability, the extinction risk employed in LIME is defined as the inverse number of the average years from the present until the extinction of a threatened vascular plant. A statistical model developed by Matsuda (2000) and refined further by Matsuda et al. (2003) [based on the Red Data Book (RDB) in Japan, Environment Agency of Japan 2000] is applied to estimate the extinction probability. The damage factor corresponding to the land use location is established by assessing regional biodiversity using the distribution of the RDB public species called the hot spot map, accessible via the Internet from the Biodiversity Center of Japan (Environment Agency of Japan 2000).

Table 3 Category endpoints considered in LIME in relation to impact categories and safeguard subjects

2.1.2.2 Damages to primary productivity

NPP loss due to land use is derived by subtracting the actual NPP from the potential NPP. NPP loss due to land use change is assessed in terms of the potential decrease of NPP based on when the former area of land use would be recovered, after accounting for the time necessary for recovering an area’s potential. The recovery time is set according to the results reported by Numata (1987). The Chikugo model (Uchijima and Seino 1985) including climatic data is applied to the calculation of potential NPP. Field-surveyed NPP data compiled by Iwaki (1981) was utilized for the actual NPP (Table 3).