Measuring future dynamics of genuine saving with changes of population and technology: application of an integrated assessment model

Abstract

Theoretical and empirical studies have been conducted on the genuine saving (GS) based on neoclassical economic theory to assess sustainable development (SD). However, only market prices and statistical national accounts have been used in empirical studies due to limited data availability. The data availability limits to measure GS only in the past and current, causing a wide gap with theoretical results. In this paper, we propose computing GS using an integrated assessment model (IAM) as connected to the mainframe model of macroeconomy. This enables us to use shadow prices, rather than market prices, obtained through an IAM, which ensures substantial consistency among variables. An example would be endogenous capital–output ratio and the rate of TFP. Also, our indicator of GS is more comprehensive in that they now account for various resources, environmental degradation, and land use. Our simulation results, with a particular focus on GS with population change (GSn) and with technological change as well (GSnt), show a sustainable future for up to the end of the century thanks to declining population in the latter half of the century and technological progress, although GS without accounting for population and technology tend to be negative, driven by, among others, capital depreciation and net primary productivity degraded by land use.

Appendices

Appendix 1: The model used in this study

1.1 Needs to extend an existing IAM used in energy and climate change assessment to the one to measure GS, assessing environmental and resources comprehensively

The IAM we used in this study (GRAPE/LIME) is a modified model of the existent IAM by merging LIME with GRAPE, and with an originally developed module of a non-fuel mineral resources balance model (see Fig. 6).

Fig. 6

Framework and data linkages among models in the GRAPE/LIME model

When the IAM model was compared with the methodology for GS measurement, we found the following are needed to apply the model to the measurement: (i) the coverage of resources and environment impacts are required to extend that in the existing IAM model for assessment of GS, some of which have little relation to global warming (e.g., some base metals like copper). (ii) environmental impacts should be assessed by marginal willingness to pay (MWTP), though the existing IAM model focuses on the impacts in detail; on the contrary, the GS measurement have wide range of environmental impacts.

We hence came to the idea that (i) mineral resources balance model should be originally developed, especially base metals and limestone that are compatible to long-term scenario analysis as well as that relate to economic growth other than energy resources, (ii) a lifecycle impact assessment (LCIA) model developed in Japan (named LIME, see Appendix 2), resembles to but different from Extern E, is best suited to widen the coverage of environmental impacts since it covers substances and inventories more than two thousands, aggregated them into four endpoints impacts via dose–response (DR) relationship, and valuing them by MWTP obtained from conjoint analysis via social survey to hundred people in Japan 2006.

Published papers only describe on second issue (merging GRAPE with LIME); one for methodological one in (Kurosawa et al. 2007), the other for a model development to couple the IAM with LCIA and applied to measure GS without non-fuel mineral resources (Kosugi et al. 2009).

1.2 Models to obtain costs of intermediate inputs

Main body of the model is described in the text. Costs of intermediate inputs used to measure GS are described in this appendix.

1.2.1 Fuels and non-fuel minerals (FC, NFC)

The energy model and non-fuel mineral resources balance model calculates discounted sum of supply costs, respectively, to determine structure of demand and supply balance of fuels and non-fuel minerals (nfm) by minimizing supply costs of them, in which supply deals with mining, milling, dressing, smelting, and refining for nfm, the conversion process of either electrical or chemical for energy, transportation among the ten global regions, to the final demand of both energy (EL _rg,yr and NE _rg,yr) and materials (MD _{sec,nfm,rg,yr}) by three representative manufacturing sectors (electricity and machinery, construction and building, motor cycles). The nfm exist as in-use stocks of product goods during the assumed products lifetime, after which they become out of use stocks, and then are finally disposed or recycled.

1.2.2 Land use and land-use change (LUC)

The land-use model calculates discounted sum of supply costs of the endogenous land use (forestry, grass land, crop land, urban, others) and land use changes, by satisfying exogenous demand for food and area of urban land (i.e., land area requirement for human settlement), by the use of exogenous costs of land rent, land conversion, and food production. The food demands are expressed as both calorie base and protein base, which are satisfied by crop productions in cropland and by meat productions in grassland. Each production is converted by use of yield to area of crop land and grass land (pasture land). The area of urban land is calculated from population and population density. Forest area is calculated via (i) deforestation and reforestation due to carbon release and absorption, (ii) conversion to crop land and grass land for food production requirements. The land category of “other” includes all others including such terrains as desert and reservation land, whose area will be kept constant.

1.2.3 External damage costs (DC)

Damage cost (DC) can be calculated by:

$$ {\text{DC}}_{\text{rg,yr}} = \sum\limits_{\text{sgo}} {{\text{WF}}_{\text{sgo,rg,yr}} \cdot \sum\limits_{\text{sbs}} {{\text{DR}}_{\text{sgo,sbs,rg,yr}} \cdot {\text{Inv}}_{\text{sbs,rg,yr}} } } $$

(A1)

where

$$ {\text{WF}}_{\text{sgo,rg,yr}} = {\text{WF}}_{{{\text{sgo,JPN,yr}}_{0} }} \cdot \left( {{\frac{{{{{\text{GDP}}_{\text{rg,yr}} } \mathord{\left/ {\vphantom {{{\text{GDP}}_{\text{rg,yr}} } {N_{\text{rg,yr}} }}} \right. \kern-\nulldelimiterspace} {N_{\text{rg,yr}} }}}}{{{{{\text{GDP}}_{{{\text{JPN,yr}}_{0} }} } \mathord{\left/ {\vphantom {{{\text{GDP}}_{{{\text{JPN,yr}}_{0} }} } {N_{{{\text{JPN,yr}}_{0} }} }}} \right. \kern-\nulldelimiterspace} {N_{{{\text{JPN,yr}}_{0} }} }}}}}} \right)^{\sigma } $$

(A2)

sgo = human health, social capital, net primary production (NPP), and biodiversity, sbs = greenhouse gases, ozone depletion substances (ODS), extraction and disposal of nfm, LU&LUC the weight factor, WF (or MWTP), and the dose–response relation, DR, are exogenously given by LIME. They are related to four endpoints (or safe guard objects) by way of DR relationship described in (Itsubo et al. 2000), then aggregated into monetary term by WF obtained through conjoint analysis (Itsubo et al. 2005). INV is inventories treated in the model, such as CO₂, SO_X, NO_X from fuel combustion, CO₂ release via deforestation, five kinds of non-CO₂ greenhouse GHG (NCGHG), fourteen kinds of ozone depletion substances (ODS), extraction and disposal of nfm, LU&LUC. NCGHG and ODS are exogenous, all the others are endogenous. DR and WF in LIME is adjusted, compatible to all regions and time steps in order to merge GRAPE with LIME. The WF is transferred by using benefit transfer expressed in Eq. (A2) [income elasticity σ of 0.5 from (Pearce 2003)].

Appendix 2: Illustrative description of impact categories treated in GRAPE/LIME

2.1 Global warming

In order to develop damage functions for the safeguard subjects of human health (Itaoka et al. 2002) and social welfare (Uchida et al. 2002), (1) damage due to the impact pathway at the time of doubled CO₂ concentration was estimated as a benchmark, while global mean temperature was projected using the DICE model (Nordhaus 1994) and (2) time series impacts were estimated by interpolation and extrapolation based on the benchmark impacts considering regional population change (United Nations 2003) and economic development (Nakićenović et al. 1998); (3) the damages were aggregated to estimate impacts per GHG emission.

2.1.1 Damages for human health

For thermal/cold stress, a dose–response coefficient between daily maximum temperature and mortality was expressed as a function of regional GDP per capita and annual average air temperature, applying a Japanese coefficient as the reference coefficient (Honda et al. 1998). For malaria, the population in malarial risk areas with and without climate change as simulated by Matsuoka and Kai (1995) was used to estimate the rate of population increase at malaria risk per 1°C temperature rise. The increase rate of dengue risk due to temperature increase is assumed to be double that of malaria, based on the study of Martens et al. (1997) who estimated that the rate of increase in the endemic potential for dengue due to temperature increase was 2.2 times that for malaria. For natural disasters, LIME referred to the expert judgments applied by ExternE (European Commission 1999) that determined that damages by typhoons would increase by 25% and damages by other natural disasters would increase by 10% at the time of a 2.5°C increase in global mean temperature. The increase in the number of people at risk of hunger due to temperature increase was estimated based on the report by Parry et al. (1999) (see Fig. 7 and Table 3).

Fig. 7

The framework of the LIME model

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

2.1.2 Damages for social capital stock

Future crop production up to the point of doubled CO₂ concentration, not considering a CO₂ fertilization effect, was 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 was calculated based on the study by 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 were calculated and the interaction between economic growth and heating and cooling energy consumption was analyzed using empirical energy consumption data for Japan (EDMC/IEE 2002). The land elevation dataset ETOPO5 accessible via GRID-Tsukuba, originally developed by the NOAA National Geophysical Data Center (NGDC), was used to calculate the areas of submergence in the case of a 0.5-meter sea-level rise that plausibly corresponds to a doubled CO₂ concentration in 2100.

2.2 Land use

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

2.2.1 Damages to biodiversity

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

2.2.2 Damages to primary productivity

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