In a previous post on understanding natural capital accounting, we presented the System of Environmental-Economic Accounting (SEEA) Central Framework as an important advance in support of evidence-based policy and analysis. The SEEA is a framework, consistent with the United Nations System of National Accounts, for organizing data describing the interactions between the environment, economy and people.The SEEA helps us overcome two critical challenges in the management of our planet’s natural resources: 1.) You cannot manage what you don’t measure, and; 2.) GDP, a standard measure of economic performance and preoccupation of policy makers, tells us little about the underlying stock and quality of natural resources underpinning economic development prospects.
In another post, WAVES Economist Juan Pablo Castañeda reminds us of the analogy of the earth as a spaceship with a finite stock of environmental resources. The SEEA provides a framework for accounting for the contribution of the environment to the economy, thereby reducing what has been referred to as the ‘invisibility’ of nature’s contribution, and enabling its rational management. SEEA-based adjusted measures of GDP allow the concerned observer- Juan Pablo’s ‘vigilant astronaut’- to discern the difference between two countries with similar levels of GDP, but different quantities and qualities of remaining stocks of natural resources which have inescapable implications for maintaining our spaceship spaceborne.
A number of countries worldwide have been developing their environmental accounts under SEEA including some developed countries such as Canada, Australia, the Netherlands, and other developing countries such as Rwanda, Indonesia and Guatemala. Experience with environmental accounting is increasing fast. On the supply side, while developing capacity and the institutionalization of environmental accounting is critical, it is equally important for statistical agencies to engage in dialogue with the end users to deliver statistics that can be used for analytical purposes that meet priority demands and provide policy and decision makers with timely, robust and relevant evidence-based policy advice.
Since 2014, the IDB has been pioneering the integration of data organized under the SEEA into economy-wide models (specifically, dynamic computable general equilibrium models- CGE models). In a 1965 seminal paper by Professor Ronald W. Jones (cited 1,472 times, plus one if a blog post counts), Professor Jones reflects on (and foreshadows the increasing relevance) of CGE models as the workhorse of policy analysis. While the application of CGE models has influenced international trade policy enormously, applications in the environmental realm are becoming just as significant. Developed with BIO Program support, the Integrated Economic-Environmental Modelling (IEEM) platform represents the culmination of efforts to integrate SEEA with economy wide models.
Currently, we are piloting IEEM in Guatemala, the country with the most comprehensive accounts in the region. IEEM is the first highly disaggregated and regionalized economy-wide model of its kind where users can simulate public policy and investment proposals, with an internally consistent and comprehensive economic-environmental data structure, and evaluate trade-offs in terms of both economic and environmental criteria. To illustrate the analytical capabilities of IEEM, we apply it to the analysis of Guatemala’s fuelwood sector which is recognized by the Government as a key sector requiring reorientation and policy intervention.
Over two million Guatemalan households use fuelwood as a primary source of energy, while it comprises 57% of the overall energy use. Fuelwood, however, is increasingly scarce, with the current deficit of 10 million m3 met by deforestation and forest degradation. Inefficient use of fuelwood, primarily in cookstoves, increases the probability of respiratory illness by 31%; causes the premature death of over 5,000 people per year, and; results in productivity losses of around 1% of Gross Domestic Product. To address this critical issue, the government has implemented a National Strategy for Sustainable Production and Efficient Use of Fuelwood.
We apply IEEM to evaluate the impacts of the fuelwood strategy by simulating a 25% increase in the efficiency of household fuelwood use (efficiency scenario). To demonstrate the potential health benefits of improved efficiency and based on findings from the literature, we implement a 0.125% increase in labor productivity to simulate household members missing less work or school, and spending less time gathering fuelwood (efficiency + health scenario). This shock is implemented gradually beginning in 2016 and reaching its full magnitude in 2020 after which the shock remains constant at 0.125%.
With households disaggregated according to urban and rural, and income quintiles, analysis of fuelwood consumption in the base year shows that poorer rural households in the low income quintile spend a greater share of their income on fuelwood (13%) compared with higher income rural households (11%) and of course compared with urban higher income households (less than 2%).
Figure 1 shows household consumption as an indicator of well-being; the improvement in fuelwood efficiency has a positive impact on well-being on the order of 0.19% with respect to the baseline by 2025 in the efficiency scenario and 0.30% in the efficiency + health scenario.
Figure 2 depicts household energy consumption. There is a 12.6% decline in the value of fuelwood consumption which remains relatively steady after the full implementation of the fuelwood strategy. There are small increases in the consumption of other forms of energy and a larger positive impact on the overall energy consumption bundle. This effect is driven by the decrease in the cost of the energy bundle as well as an income effect due to the savings on fuelwood consumption. Impacts on energy consumption in the efficiency + health scenario is similar in trend and magnitude to those presented in Figure 2.
Figure 3,shows the greenhouse gas emissions captured in the Guatemalan SEEA, namely carbon dioxide, nitrous oxide and methane, and; their decline as a result of the efficiency scenario. The efficiency + health scenario, with a similar level of fuelwood consumption, provides a similar result in terms of emissions.
Figure 4 demonstrates that those households consuming a greater share of fuelwood, particularly the poorer rural households, have the greatest reduction in terms of their emissions and therefore can be expected to benefit the most from the fuelwood strategy in terms of savings shares as well as health benefits.
Figure 5 shows the multidimensional impacts of the fuelwood efficiency scenario. The figure shows a small decline in agricultural land use with a concomitant increase in the stock of forestland as deforestation is slowed as a result of the fuelwood strategy. Forestry output declines as would be expected, as fuelwood prices fall. Water use remains similar to baseline consumption despite the small decline in agricultural output. Total greenhouse gas emissions fall as a result of the improvements in efficiency.
In summary, the application of IEEM to the fuelwood sector shows:
- Guatemala’s fuelwood strategy reduces fuelwood consumption, but increases overall energy consumption.
- The strategy enhances household well-being;positive health impacts amplify this result (the poor benefit more).
- The strategy results in a small decline in agricultural production while reduced fuelwood use has a positive impact on the stock of standing forest.
In this post, we have shown the analytical potential of IEEM and for the first time, the integration of data organized under the SEEA into an economy wide modelling framework. We continue to test various applications of IEEM, improving its sectoral treatment of environmental resources, and developing a generic version that may be applied to other countries with SEEA data.
The BIO Program’s goal through this line of research is to demonstrate to the region and beyond, the importance of environmental accounting and the analytical power the IEEM approach brings to bear on complex policy issues. The Program aims to shift the discussion of policy and investment impacts from a growth-centric perspective to a more holistic appreciation of how interventions impact the wealth of nations. Through the development of IEEM, the BIO Program is linking the supply of environmental statistics with end user demand, innovating in order to ‘Deliver on the Paradigm of Evidence-based Policy Advice’.
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