This paper attempts to connect the measurement of social progress from the Stiglitz report and climate change mitigation (CCM) by the Intergovernmental Panel on Climate Change (IPCC) assessment reports. Each report has been addressed insufficiently on the issue, although both reports have common interests in development patterns and pathways for the economy, humanity, and society. This study used our original integrated assessment model and applied for measuring various indicators for sustainable development, such as genuine savings (known as GS) and human appropriation of net photosynthetic primary production (HANPP). We expanded an analysis of sustainable development indicators of quality of life (QoL) and of the human development index (HDI) and introduced a modified quality of life indicator. These indicators expand on the “classical” GDP loss, which has been well analyzed in the majority of CCM literature. Our model’s main framework is based on the Regional Integrated model of Climate and the Economy (RICE) extended from Ramsey-Cass-Koopmans with a simplified climate model and added three original resource balance models with environmental consequences with a life cycle impact assessment (LCIA) model. We prepared various climate policy scenarios ranging from business as usual to economically efficient, CO_{2} double stabilization, and targeting two degrees Celsius (DC). We believe this work has three contributions. First, in contrast with the World model by the Limits to Growth, our model has an economic foundation where genuine savings is introduced. Second, while the Stiglitz report only extrapolates the current genuine savings trend, we are able to calculate the future trajectories of sustainable development indicators, based on a sophisticated integrated assessment model. Third, when compared to the RICE analysis, which sought the optimal climate policy in the sense of cost - benefit analysis, our model introduces indicators of sustainable development in assessing climate policies.
The Stiglitz report [
The measurement of QoL is categorized into three approaches: subjective well-being (SWB), capabilities, and welfare economics and fair allocations. SWB includes three separate aspects: life satisfaction, the presence (and absence) of positive feelings, and the presence (and absence) of negative feelings. Two representative measurement approaches are the World Value Survey (a qualitative survey) and the Gallup World Poll (a quantitative survey with a 0 to 10 quality-of-life scale). Quality of life is comprised of material living standards (e.g., income), health, education, personal activities (e.g., paid and unpaid work, commuting, leisure time, and housing), political voice and governance, social connection, environmental condition, and insecurity. These conditions can be functioning (people’s doings, e.g., working and commuting, and people’s being, e.g., healthy and educated) or freedom (e.g., political voice and participation). The human development index (HDI) has a capability approach in terms of a person’s freedom to choose among the various combinations of functioning factors. The HDI is the representative indicator aggregating across those various domains, specifically health, education, and living standards.
SD & E covers various economic and environmental indicators—things like dashboards or composite indexes, adjusted GDP, adjusted net savings (ANS), and ecological footprint (EF). As recommended in the Stiglitz report (Message 1, p. 263 of the report), measuring SD & E requires future projections as well as historical observations. Appendix 3 of the report (pp. 280-282) shows some modifications on ANS estimates up to the year 2030 with trends observed since 1990 or remaining constant at the 2006 level in ANS, excluding CO_{2}damage or its intensity ratio (CO_{2}emissions per units of GNI). Appendix 4 of the report (pp. 283-287), titled “More on the ANS and Climate Change”, offers economic valuation of global warming consequences by so-called climate welfare economy integrated assessment models (IAMs). It illustrates some of the narrative and dynamic scenarios on ANS with and without a climate policy.
None of the qualitative in-depth investigations by IAMs, however, have been addressed in the report. Contrary to this, climate policy assessment has long been analyzed only by “classical” loss of GDP or the social cost of carbon (SCC) (calculated as the present value of discounted economic welfare loss by a marginal GHG emissions increase) [
The organization of this article is as follows. Section 2 gives short reviews on the theoretical background and measurement issues for the economic and environmental indicators. Section 3 describes our IAM and calculation of the indicators. This is followed by results, discussion, and conclusions in Sections 4, 5, and 6, respectively.
Genuine Savings (GS) was established as a comprehensive indicator of sustainability in successive theoretical contributions—from early studies by Dasgupta and Heal [
GS is effectively the rate of change of the total wealth available in an economy. This total wealth is understood as comprehensive wealth, that is, the economic value of all the capital stocks with and without market value in a given economy. GS therefore integrates all of the changes (impacts) that alter the ability of an item to yield its services.
GS is grounded in the standard utility theory; as a result, it is easily incorporated into the DICE model [
V t = ∫ t ∞ U t ( C ( s ) ) e − ρ ( s − t ) d s (2.1-1)
“Sustainable development” as presented in the WCED report [
Comprehensive wealth is defined in Equation (2.1-2), where W_{t}is comprehensive wealth valued using the shadow prices of the three capital stocks. Genuine Savings is then the rate of change of capital stocks, valued at their shadow prices, as shown in Equation (2.1-3).
W t = ∂ V t ∂ K t K t + ∂ V t ∂ H t H t + ∂ V t ∂ P t P t (2.1-2)
G S t = ∂ V t ∂ K t d K t d t + ∂ V t ∂ H t d H t d t + ∂ V t ∂ P t d P t d t (2.1-3)
Two methods produce empirical estimates of the theoretical notions of “Genuine Savings” and “Comprehensive Wealth.” The first method, Genuine Savings, which is outcome based, is used by the World Bank, while the second method, Comprehensive Wealth, which is capability based, has been used by the UN in a series of reports on inclusive wealth based on Arrow et al. [
The merits of both approaches are now routinely debated in the literature, as exemplified by the 2012 special issue of Environmental and Development Economics [
Attention was first paid to HANPP in the 1970s to raise the concern over excessive human economic activities (Whittaker and Likens, 1973) [
NPP provides ecosystem services through agriculture and forestry, some of which can replace fossil fuel products (e.g., biofuels). Moreover, it also has a capacity to absorb exhaust emissions. NPP also serves as a buffer for waste products [
The foundations of the capability approach taken by HDI are strongly rooted in philosophical notions of social justice; they focus on human ends and on respecting an individual’s ability to pursue and realize personal goals. The HDI is the geometric mean of normalized indices for each of the three dimensions as a proxy of human development, namely, having a long and healthy life, being knowledgeable, and having a decent standard of living. The health dimension is assessed by life expectancy at birth (LEB); the education dimension is measured by the mean of years of schooling (MYS) for adults aged 25 years and older and the expected years of schooling for children of school-entering age. The standard of living dimension is measured by GNI per capita. The HDI uses the logarithm of income to reflect the diminishing returns from increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using the geometric mean. This is illustrated in Equations (2.2-1) through (2.2-6) [
Healthindex = ( LEB j , t − 20 ) / ( 85 − 20 ) (2.2-1)
Educationindex = ( EYSindex + MSYindex ) / 2 (2.2-2)
where the EYS index (expected years of schooling) = ( EYS j , t − 0 ) / ( 18 − 0 ) (2.2-3)
MYSindex = ( MYS j , t − 0 ) / ( 15 − 0 ) (2.2-4)
Incomeindex = [ ln ( GNI j , t / cap j , t ) − ln ( 100 ) ] / [ ln ( 75000 ) − ln ( 100 ) ] (2.2-5)
HDI = [ ( Healthindex ) ∗ ( Educationindex ) ∗ ( Incomeindex ) ] 1 / 3 (2.2-6)
This mathematical expression has well-known limitations. One limitation is the weighting among the three dimensions in applying the geometric mean; notably, that weighting the importance of the three dimensions implies is an arbitrary value judgment. Because of this algorithm, HDI’s movements have tended to be dominated by changes in the GNI component, at least for developed countries (such as France and the United States) with high performance in the health and education domains. Another limitation is applying logarithm (or nonlinear) GNI/cap; it implicitly values an additional year of LEB in each country by its GNI/cap (the higher income like that in the United States has greater value than the lower income of India or Tanzania).
To overcome shortcomings in the well-established HDI, a modified QoL indicator is proposed [
Q o L f a = 0. 828 ∗ M Y S + 0. 918 ∗ GDP + 0. 925 ∗ G N I + 0. 913 ∗ I H R + 0. 91 0 ∗ L E B + 0. 685 ∗ I W A (2.2-7)
Q o L f a = 0. 831 ∗ M Y S + 0. 878 ∗ GDP + 0. 932 ∗ I H R + 0. 924 ∗ L E B + 0. 714 ∗ I W A (2.2-8)
omitting GNI term.
Our modeling strategy is based on an IAM, combining a RICE model for climate change assessment with the LCIA model (named LIME, life cycle impact assessment method based on endpoint modeling [
u ( c j , t ) = { C j , t 1 − η 1 − η ( η ≠ 1 ) log c j , t ( η = 1 ) (3.1-1)
where u ( c j , t ) is the per capita utility of consumption in region j at time t. The parameter η is the elasticity of the marginal utility of consumption^{3}. The total regional utility, u ( c j , t ) ,is obtained by multiplying individual utility by P j , t ^{4}, the exogenously given population number for region j in time t. We then sum the regional total utility for all future time periods s over the time horizon T to obtain intertemporal well-being, V j , t ,shown in Equation (3.1-2).
V j , t = ∑ t T P j , s u ( c j , s ) ρ ( s − t ) (3.1-2)
where ρ is the pure rate of time preference, reflecting how future generations’ well-being is taken into account. Each region is assumed to produce a single commodity, which can be used for either “generalized” consumption or investment as economic variables. The generalized consumption includes not only traditional market purchases of goods and services but also nonmarket consumption, such as enjoyment of the environment. Finally, regional-level intertemporal well-being, V t is maximized at the aggregate level via the function W t in Equation (3.1-3):
max W t = ∑ j n N e g j V j , t (3.1-3)
where W t ^{5}is the objective function weighted sum of social welfare, V j , t ,for region j, t by Negishi weight, N e g j ^{6}. The use of Negishi weights means that the distribution of well-being is kept constant over time, preventing convergence in consumption levels.
Gross output is determined by a nested production function, with capital, labor, and natural resources as inputs:
F ( A j , t , K j , t , H j , t , E L j , t , N E j , t , M j , t , L R j , t ) (3.1-4)
where A j , t is the exogenously given total factor productivity term, K j , t is physical capital, H j , t is human capital, E L j , t is electricity, N E j , t is nonelectric energy, M j , t denotes nonfuel mineral resources, and L R j , t denotes land resources.
We assume that all transfers of production factors across regions happen through investment and divestment: there are no lump sum transfers of capital, making it effectively immobile. Physical capital accumulation and depreciation happen only through the usual equation of motion:
K j , t + 1 = ( 1 − δ ) K j , t + I j , t (3.1-5)
where δ is the annual rate of capital depreciation. In line with our representative agent assumption, we take population growth and technological change to be exogenous. Technological change in the model is divided in two parts: the exogenously given TFP and the evolution of the mix of inputs used in the production process.
A j , t + 1 = A j , t ( 1 + τ j , t ) (3.1-6)
where A j , t ,the TFP, is determined every period based on the exogenous TFP growth rate, τ j , t . Capital accumulation and natural resource inputs are then determined by maximizing the discounted utility flow over time constrained by the technology mix (the production function). Net output is then given by:
Y j , t = F j , t − T C j , t − E X T j , t (3.1-7)
The net output Equation (3.1-7) ties together the three components in
The macroeconomic model in the red box determines gross output, based on the cost of resource acquisition, T C j , t ,as determined in the blue box. Available output is then reduced by an estimation of the external cost of production, E X T j , t ,which is determined by the LCIA model (yellow box) to get net output, Y j , t . Further details on T C j , t and E X T j , t are provided below. There is interregional trade of the final good, and trade is not balanced. Thus, the accumulated trade surplus/deficit of each region is not necessarily zero in any period, including the final period. The budget constraint for the representative agent in each region is therefore:
Y j , t = C j , t + I j , t + M j , t − X j , t (3.1-8)
With the imports, M j , t ,and the exports, X j , t ,interregional trade is then balanced globally, by the next equation:
∑ j n M j , t = ∑ j n X j , t (3.1-9)
Because trade is not constrained by a requirement to be balanced at any time, exogenous constraints reflecting the feasible evolution of consumption and investment are imposed every period for every region j. The net output is allocated between generalized consumption C j , t ,and investment in physical capital, I j , t ,in a way that maximizes the aggregate (world level) well-being, W t . The level of Y j , t that is selected is associated with a cost and a level of environmental impact in the blue and yellow parts of the model.
The total cost of production, T C t comes from the three models of resource balance in the blue box (
For simplicity, each model generates one final total cost, which is the product of cost minimization using dynamic linear programing. In Equation (3.1-10), F C j , t is the production cost of fuel, N F C j , t is the cost of nonfuel resources, and L C j , t is the cost of land resources. Formally,
T C j , t = F C j , t + N F C j , t + L C j , t (3.1-10)
The external cost of production, E X T t ,comes from the LCIA model, LIME3, shown in the yellow box in
E X T j , t = ∑ M W T P j , t ∑ D R j , t I n v j , t = ∑ M W T P j , t ⋅ E P j , t (3.1-11)
The external cost is best understood as a stock/impact/value relationship. I n v j , t (stock) represents inventory releases, which can be expressed as a function of P t (e.g., CO_{2}emissions via transformation of energy resources stock changes, land cultivation and waste disposal by mining, and disposal of mineral resources). D R j , t (impact) is a function to express the dose-response relationship (or cause–effect chain) [
M W T P 0 is a set of marginal willingness to pay (MWTP) associated with the endpoints. We use MWTP instead of estimating a damage function because an estimation of an aggregated function is ill-suited to our disaggregated modeling structure. See Itsubo et al. [
The discrete time step of the model is 10 years, and 10 regions are included: North America, Western Europe, Japan, Oceania, China, Southeast Asia (the Association of Southeast Asian Nations (ASEAN) member countries, plus India), the Middle East and North Africa, sub-Saharan Africa, Latin America, and the former Soviet Union and Eastern Europe. The population is assumed to be composed of representative agents, with the size estimations based on UN projections from the Shared Socioeconomic Pathways (SSP)-2 scenario [
Regarding the labor population ( L j , t ), we computed the population rate at each time period for each region, based on a medium-scenario population projection by the United Nations [
The setting of the initial K value was obtained from the RICE 2010 model [
The utility discount rate, r, is assumed to be 1.5% per annum, in the lower end of the range in the literature (0.1% - 5%) [
TFP was calibrated from data sources to fit the scenarios (level of production). The form of function ϕ is increasing but diminishing in rate ( ϕ ′ = d ϕ / d S > 0 , ϕ ″ = d 2 ϕ / d S 2 < 0 ), where ϕ equals 0 when S is 0. Here, ϕ ′ denotes the marginal income increase by additional education attainment, corresponding to the coefficient (rate of return); ϕ ′ was determined using data from various studies [
We did not follow DICE 2013 for the initial values for the TFP level ( A j , 0 ) and growth rate ( τ j , t ). We calibrated the TFP growth rate based on the future baseline scenario (SSP-2) to obtain feasible solutions for computation. In some sections, we applied historical data from Klenow [
First, we obtained A j , t from the solution of Equation (3.1-7), shown here in expanded form:
Y ¯ j , t = ( A j , t , K j , t , H j , t , E L j , t , N E j , t , M j , t , L R j , t ) − ( F C j , t + N F C j , t + L C j , t ) (3.2-1)
where Y ¯ j , t is the GDP of SSP-2 and the other selected variables are endogenously calculated in the model. Subsequently, regression analysis was conducted from the obtained variable A j , t ,to derive the initial level ( τ j , 0 ) and the decline rate of the TFP. The derived TFP is given as a constant parameter throughout our simulations.
The model as presented so far is the baseline scenario. In this setting, all externalities are internalized, and all of the parameters are set at their base level. This is called the economically efficient scenario (Eeff). The incentives to reduce CO_{2}emissions are based on their direct and indirect cost through T C j , t and E X T j , t . Next to the baseline, we define a business as usual (BAU) scenario. Under BAU, the externalities associated with the endpoints have not been taken into account by the social planner; therefore, E X T j , t = 0 . Then, the budget constraint in 3.1-8 becomes:
Y j , t = F j , t − T C j , t (3.3-1)
Our third and fourth scenarios are based on opposite trajectories for carbon emissions. The CO_{2}double scenario (CO_{2}) is obtained by adding the cumulative emissions from 2010 to 2150 of the WRE-550 scenario [
We then computed GS ex post following the method used by the World Bank [
G S j , t = ( I j , t − δ K j , t ) + ( I m j , t + I e j , t ) − ∑ M W T P j , t ⋅ E P j , t − ∑ S P j , t ⋅ I n v j , t (3.4-1)
In line with the theoretical definition in Equation (2.1-3), GS is the rate of change in capital stocks, at current shadow prices. I j , t is investment in physical capital, δ K j , t is the depreciation of physical capital, I m j , t is investment in health capital^{8}, I e j , t is investment in human capital, ∑ M W T P j , t ⋅ E P j , t is the depletion of exhaustible resources (natural capital), and ∑ S P j , t ⋅ I n v j , t is the indirect impact on the well-being of natural capital depreciation (environmental degradation). These values are calculated from simulation results using the following formulas:
· I j , t − δ K j , t was determined by Equation (3.1-5);
· I m j , t was estimated using a power function defined using the World Development Indicators (WDI). GDP values were then entered into that function to estimate the value of investment in medical expenses per capita. This per capita value is then multiplied by the population size in time t to obtain the total investment value;
· I e j , t was estimated using a linear function defined using WDI. The Y j , t values were then entered into that function to estimate the value of investment in education per capita in year t. This per capita value was then multiplied by the population size in time t to obtain the total investment value;
· I n v j , t was the natural resource stocks and inventories obtained from the LIME3 and RICE components of our model.
The shadow prices associated with produced, human, health, and natural capital were the optimal prices obtained from our model. The S P j , t prices were computed as the rate of change in global well-being when the relevant inventory varies, over the change in well-being when consumption varies where I n v j , t was also taken straight from the model results as in Equation (3.1-11).
S P j , t ≡ ∂ W / ∂ I n v j , t ∂ W / ∂ C j , t = W i n v j , t W C j , t (3.4-2)
We have two sets of shadow prices, M W T P j , t and S P j , t ,for the two types of natural capital flows: direct flows from inventories and indirect flows from endpoints. We did not compute shadow prices for produced and human capital; instead, we directly added the full investment value. Our GS estimates were computed based on a 10-year step.
It should also be noted that due to the structure of the model, only I j , t − δ K j , t was derived directly from the optimization process. The other investment values were subtracted from the final level of consumption, based on net output.
The intuition was as follows: the representative agent set the level of net investment in produced capital which yielded the available produced capital stock in t K j , t . During this first step, a gross level of consumption was set, from which investment in human capital and health capital should be deducted. The agent has no control over this lump sum subtraction to gross consumption, because the amount is exogenously set, proportionally to Y j , t . The actual value of consumption is this net amount.
We can now define Genuine Savings as the rate of change in total wealth, by first computing total wealth using the World Bank [
W j , t , T O B / W B = ∫ t T C j ( t ) e − ρ ( s − t ) d s (3.4-3)
where ρ is equal to 1.5 and C is defined as sustainable consumption, that is C minus I m j , t and I e j , t . Gross genuine savings is therefore:
Δ W j , t + 1 , T = [ ( I j , t + 1 + I m j , t + 1 + I e j , t + 1 − δ K j , t + 1 ) − ∑ M W T P j , t + 1 ⋅ E P j , t + 1 − E X T j , t + 1 ] / W j , t , T O B / W B (3.4-4)
This shows how wealth has increased between t and t + 1. We then adjusted this rate of change for population growth and technological progress (both exogenous in our model):
Δ W n t j , t + 1 = Δ W j , t + 1 − p j , t + 1 + τ j , t + 1 [ % / t ] (3.4-5)
With p j , t + 1 ,the population growth rate^{9}, and τ j , t + 1 ,the technological progress growth rate, Δ W n t j , t ± 1 is the notation for the final fully adjusted rate of change in wealth, or the “GS rate.”
The denominator of HANPP is the potential NPP, determined by temperature and precipitation [
· Low estimate: Direct consumption (demanded quantity) of agricultural products (i.e., rice, wheat, corn), wood (i.e., logs, wood pulp, timber/boards, paper), and seafood eaten by humans and livestock;
· Middle estimate: The harvested amount from agricultural land, grassland, forests, etc., that produces the direct consumption (i.e., low estimate). This level is calculated as the sum of the direct consumption and conversion loss (unused residuals);
· High estimate: This is the sum of the middle estimate and the potential loss of NPP due to LU and LUC.
Direct consumption for the low estimate and the conversion loss in the middle estimate were calculated using our simplified land-use model. The potential NPP by 2100 that is needed to calculate the high estimate was obtained from the Chikugo model [
It should be noted also that our model does not have as high a resolution as the country or grid level using GIS, as seen in papers from the Special Issue of Ecological Economics in 2009 [
We followed the equations from (2.2-1) to (2.2-6) in which GNI_{j}_{,t}in (2.2-5) is substituted for G D P j , t . L E B j , t , E Y S j , t ,and MYS_{j}_{,t}were expressed by the semilogarithmic functions of GDP_{j}_{,t}/cap_{j}_{,t}. The endogenously obtained GDP_{j}_{,t}was input into the functions to calculate the HDI_{j}_{,t}where j corresponds to the global level.
Similar functions in Equation (2.2-2) were derived for QoL_{fa}(j, t) in (3.4-6) by factor analysis in each time step (i.e., t = 2010, 2020, …, 2100), as expressed by Equation (3.4-6), from which a simple average is applied to indicate the global level. Similar semilogarithmic functions of GDP_{j}_{,t}/cap_{j}_{,t}were also applied to IHR_{j}_{,t}and IWA (j, t).
QoL fa j , t = a MYSt ∗ MYS j , t + a GDPt ∗ GDP j , t + a IHRt ∗ IHR j , t + a LEBt ∗ LEB j , t + a IWA ( t ) ∗ IWA j , t (3.4-6)
mitigation policies. It is apparent that some scenarios show extreme changes, while others are less sensitive. The GDP loss shows a plausible trend along with the mitigation efforts: the more stringent, the larger loss. After confirming our model’s behavior as a departure to examine the results, GSnt also shows the economically efficient path (denoted as Eeff) has the highest positive value, followed closely by the CO_{2}path. Furthermore, the business as usual line is far less level than those the prior two, while the ZERO scenario is collapsing to a negative level due to the rapid increase in various shadow prices; this suggests that the world might not accommodate such dramatic changes. HDI also shows a slight difference with consistent but not surprising trajectories: a higher GDP leads to higher HDI because its components are expressed as a function of per capita GDP. Unfortunately, our QoL_{fa}has some difficulty in explaining the trajectory. The relative “rankings” of the global average among the ten regions are unclear because the global average shown in the figure is pulled down by the massive population in Southeast Asia and sub-Saharan Africa.
The model in this study is original; it diverges from similar studies and highlights the significance of applying multiple indicator dimensions, namely, “classical” GDP loss, SD & E in both WS (i.e., GS) and SS (e.g., HANPP), and QoL (e.g., HDI). Comparable studies explore future paths over the century of HDI by the World 3 model and the GS estimate by Pezzey and Burke [
Pezzey and Burke [
make a precautionary approach, with a similar aim as the 2-degree Celsius target; however, it did not show the trajectory of GS over time, but rather, it derived SCC (the current discounted marginal value of CO_{2}), substituting the “environmental degradation” term in calculating GS.
No study has illustrated the future paths of those (or similar) indicators in SD & E and QoL simultaneously (the World 3 model illustrates EF and HDI), and the literature on CCM mostly focuses on GDP loss. In this sense, this study is significant because it simultaneously illustrates multiple indicator dimensions.
We believe this work has three contributions. First, in contrast to the World model by the Limits to Growth, our model has an economic foundation that introduces GS. Nordhaus and Gordon criticized the World model because it lacked an academic foundation, especially economic theory and mineral resource data. This shortcoming, however, has been overcome by our modeling approach. The simulation technique (i.e., systems dynamics) employed by the World model “forecasted” the trajectories of “the economy”, “HDI”, “pollution”, and “population”. The nature of that technique nature led to “overshooting”, leading to dismal outcomes. Compared to this, our model and GS are grounded by economic theory in a normative way (i.e., maximization of discounted utility flow).
Second, while the Stiglitz report simply extrapolates the current GS trend, we calculate the future trajectories of SD indicators based on a sophisticated IAM, as described above. Third, while the RICE model seeks the optimal climate policy in the sense of cost-benefit analysis, our model introduces SD indicators to assess climate policies. Nordhaus, the developer of the series of DICE/RICE models, had developed models for energy technologies [
Our modeling exercise shows that we were not fully successful in operationalizing the QoL indicators for climate policy assessment because the trajectory of HDI is well synchronized to GDP level, and our original indicator QoL_{fa}shows unexplainable behavior. The behaviors seen in SD & E indicators (GS, HANPP) are easier to interpret than QoL indicators, while GDP is the easiest to interpret. The paths suggest suitable operationalization for climate policy assessments in the current-day situation. Investigations on the choice and development of indicators, however, still do not fully respond to the social demands indicated in the Stiglitz report. Indicators for assessing climate policies are not yet operationalized.
The authors declare no conflicts of interest regarding the publication of this paper.
Tokimatsu, K., Yasuoka, R. and Nishio, M. (2019) Tackling the Stiglitz Report: Measuring Social Progress and Economic Performance under Various Climate Policy Scenarios. Modern Economy, 10, 2209-2231. https://doi.org/10.4236/me.2019.1011139