The objective of this paper is to propose a methodological framework in order to per- form second-bestanalysisinageneralequilibriumclimatechangemodel. More precisely, we focus on the study of the set of equilibria in the decentralized economy. The main difficulty of this approach lies in the way the research activity is modeled, in particular the type of innovation goods which are developed as well as their pricing. In the standard endogenous growth theory (Aghion and Howitt, 1998; Romer, 1990...), when an innovation is produced, it is associated with a particular intermediate good. However, the more often, embodying knowledge into intermediate goods becomes inextricable in more general com- putable endogenous growth models with pollution and/or natural resources. In addition, those technical difficulties are emphasized when dealing with several research sectors, i.e. when there are several types of specific knowledge, each of them being dedicated to a par- ticular input (resource, labor, capital, backstop...) as it is proposed in Acemoglu (2002). To circumvent those obstacles, we assume that the pieces of knowledge are directly priced (see for instance Grimaud and Rougé, 2008). We compute the social and the market values of an innovation and we suppose that the policy-maker can reduce the gap between these two values owing to dedicated R&D subsidies.
Our benchmark computable generalequilibriummodel is a standard one in- spired from the model of Decaluw´ e et al. (2001) developed by Cockburn et al. (2006). This basic structure will however be deeply modified in order to de- scribe adequately the behaviour of labour market and the internal and external determinants of migratory flows. Very briefly, our version of this model contains 34 monoproductive sectors distributed between two aggregate sectors: a rural sector (agriculture and fishing) and an urban sector (industry, tradable services and non tradable services); two factors of production (a labour factor bundle of the different professional rural/urban categories mobile between rural/urban sectors and a capital factor specific for each sector); five agents (rural and urban households, firms, government, and the Rest of the World). Then, we modify the specification of the rural sector in order to distinguish, within this sector, between subsistence agriculture and industrial agriculture. Given the relative complemen- tarity of capital and labour in the public value added, the latter is modeled using a Leontief function, contrarily to private value added represented by a CES 8 . We endogenize labour supply on each segment of labour market and we take into account unemployment rates by professional categories. We further assume that unemployed persons can not change their profession. In other words, the cross elasticity of labour supply should be null. Finally, we introduce a new block of equations relative to rural and urban emigration, internal migration from rural to urban areas and Sub-Saharan immigration, and we suppose the existence of migration costs. Our model is calibrated on the SAM of the year 1998.
puts of 25 General Circulation Models run for the mid- Holocene period (6 ka BP) with a set of palaeoclimate re- constructions based on over 400 fossil pollen sequences dis- tributed across the European continent. Three climate pa- rameters were available (moisture availability, temperature of the coldest month and growing degree days), which were grouped together using cluster analysis to provide regions of homogenous climatechange. Each model was then investi- gated to see if it reproduced 1) similar patterns of change and 2) the correct location of these regions. A fuzzy logic dis- tance was used to compare the output of the model with the data, which allowed uncertainties from both the model and data to be taken into account. The models were compared by the magnitude and direction of climatechange within the region as well as the spatial pattern of these changes. The majority of the models are grouped together, suggesting that they are becoming more consistent. A test against a set of zero anomalies (no climatechange) shows that, although the models are unable to reproduce the exact patterns of change, they all produce the correct signs of change observed for the mid-Holocene.
ures provide also policy relevant information. The first measure – the decrease in private vehicle speed limitation – stimulates economic activity ina pro-environmental fashion by con- tracting GDP ina first phase but then allowing it to reach higher levels and resulting ina positive sum game. The major dynamic at play is that changes in mobility habits increase the income disposable for buying other goods and services, leads people to relocate, changes the geography of production and finally improves external economies of scale. This suggest a mechanism through which regulatory instruments are capable of moving upward nations’ production-possibility frontiers by eliminating costly organizational frictions if they release in- come that becomes available for other socially more profitable uses. The second measure is the implementation of a CO2 tax to private vehicle whose collected revenues are used to finance an increase in public transport speeds in some places. The main policy insight is that setting a price of 100 per tonne of CO2eq – be it in Dollars or Euros – represents virtually nothing once converted per commuter-kilometer and deters only marginally the use of cars, whereas the recycling of the tax in public transport infrastructure induces a low-carbon growth. Indeed, the implementation of a duly recycled carbon tax releases an important mass of income at the national scale that can be used to change the geography of attractiveness and stimulate production. In this case, a carbon tax is less interesting as a ”signal” than as a component of complex policy designs where recycled revenues help to support policies aiming at redirecting behavioral evolutions.
Physically, B might become more negative with warming for two reasons. The ﬁrst reason is speculative: the positive longwave cloud-top-height feedback may be stronger if there are more high clouds in the basic state. Based on our simulations and on observations, nonaggregated atmospheres seem to have more high clouds than aggregated atmospheres—although the causal reasons for this could relate either directly to humidity through the effects of saturation deﬁcit and cloud lifetime, or indirectly to humidity or temperature proﬁles through radiatively driven divergence in clear-sky regions (Bony et al., 2016). But asecond reason why B might become more negative with climate warming is robust: the difference in clear-sky OLR between a moist and dry atmosphere increasingly diverges with warming, in tandem with the rapid strengthening of the water vapor feedback near present tropical surface temperatures (e.g., Figure 4.32 of Pierrehumbert, 2010). As an example of how this translates to a stronger negative climate feedback in an aggregated state, consider moist adiabats with greenhouse gases of solely water vapor and 400 ppmv of CO 2 , and prescribed vertically uniform relative humidity that remains constant with warming. Based on calculations with RRTMG, an atmosphere with 70% RH has a clear-sky longwave feedback of 21.44 W m 2 K 21 between 280 and 310 K, whereas an atmo- sphere with 30% RH has a clear-sky longwave feedback of 22.34 W m 2 K 21 over the same temperature range. Comparing a nonaggregated state (with 70% RH everywhere) to a partially aggregated state (where half of the domain remains at 70% RH but the other half dries to 30% RH) would give a clear-sky longwave contribution to AdB=dT 20:45 W m 2 K 21 —comparable to our estimate above of 20.41 W m 2 K 21 for the noncloud feed- back difference between aggregated and nonaggregated states. The importance of relative humidity in dry regions is not a new result; Pierrehumbert (1995) emphasized ‘‘the degree of dryness of subsiding regions’’ as a key factor in regulation of tropical energy balance and global climate. But it is worth reiterating that aggrega- tion can inﬂuence climate sensitivity not only by changing with warming, but also by acting across all surface temperatures to dry the atmosphere overall and enhance the dryness of dry regions. Climate impacts of aggre- gation could occur through a rapid changein degree of aggregation with warming, or through a subtler inﬂu- ence on the distribution of tropical relative humidity that may already hide within the present climate.
4.2.2 The CGE analysis
The calibration of the base-year solution of our CGE model requires a consistent data set, reflecting the structure of the Tunisian economy. As existing SAMs for Tunisia are unlikely to adequately reflect the structural features of the national agricultural sector, we compiled a new SAM for the year 2001. Building a completely new SAM requires however gathering a huge amount of data; we use a top-down approach to carry out the compilation of the new SAM. Our procedure follows two main steps. First, we construct a Macro SAM from national accounts. Second, we disaggregate the Macro SAM by activity and commodity to generate a Micro SAM. The disaggregation mainly relates to agriculture and agri-food processing commodities and is implemented using the Input-Output (IO) table of 2001, the national- accounts and different complementary sources such as the surveys conducted by the National Institute of Statistics (INS), the different reports of the Ministry of Finance and Planning, and the Ministry of agriculture 22 . This step is carried out in order to match with the commodity structure of the Tunisian household expenditures, and ina way that is consistent with the national accounts and coefficients from a prior SAM. As the data discrepancies in the micro matrix may cause unbalances, we apply the cross-entropy approach to generate a balanced SAM table. Table 2 displays the macro SAM for the year 2001.
Aerosol particles in the atmosphere, such as black carbon (BC), have a significant influence on global climate. These particles both scatter and absorb radiation, thus
impacting radiative transfer through the atmosphere (direct and semi-direct e ffects). Additionally, aerosols act as cloud condensation and ice nuclei, which influence cloud properties (indirect e ffects). The first indirect effect is based on the premise that for a given amount of cloud liquid water content, increased aerosol number implies more and smaller cloud droplets, and hence a more strongly reflective cloud. The second
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Aerosol particles in the atmosphere, such as black carbon (BC), have a significant influence on global climate. These particles both scatter and absorb radiation, thus impacting radiative transfer through the atmosphere (direct and semi- direct effects). Additionally, aerosols act as cloud condensa- tion and ice nuclei, which influence cloud properties (indirect effects). The first indirect effect is based on the premise that for a given amount of cloud liquid water content, increased aerosol number implies more, and smaller cloud droplets, and hence a more strongly reflective cloud. The second indi- rect effect suggests that a cloud with more but smaller cloud droplets will be less likely to precipitate (i.e. enhanced life- time) due to a slower growth of the individual droplets. Black carbon aerosols have a role to play in all of these climate ef- fects. For this reason, the concentration of BC aerosols must be accurately determined by atmospheric general circulation models (AGCMs).
Rotmans et al. (1997), mention that the integrated assessment approach allows for an exploration of the interactions and feedbacks between subsystems and provides flexible and fast simulation tools. It also identifies and ranks major uncertainties, and supplies tools for communication between scientists, the public, and policy makers. Davies (2007) provides some examples of integrated assessment models including the Integrated Model to Assess the Greenhouse Effect, IMAGE 2.0 (Alcamo et al. 1994), the Asian Pacific Integrated Model, AIM (Matsuoka et al. 1995), the Model for Evaluating Regional and Global Effects of GHG reduction policies, MERGE (Manne et al. 1995), the Tool to Assess Regional and Global Environmental and health Targets for Sustainability, TARGETS (Rotmans and de Vries 1997), the Integrated Global System Model, IGSM (Prinn et al. 1999), Integrated Climate Assessment Model, ICAM (Dowlatabadi 2000), the Dynamics Integrated Climate-Economy model, DICE (Nordhaus and Boyer 2000), the Feedback-Rich Energy-Economy model, FREE (Fiddaman 1997; Fiddaman 2002), and World3 (Meadows et al. 2004). The list of IAMs and Computable GeneralEquilibrium (CGE) models used inclimate policy analyses is long. The reader can refer to Ortiz and Markandya (2009) and Stanton et al. (2008) for a literature review of some of these models.
tremely rigorous and closely related to human capital theory. All households value family consumption and the human capital of their offspring. Altruistic parents choose the time their child spends in school based on a careful cost-benefit analysis. On one hand, schooling in- creases the child’s human capital and future earnings on the labor market, as well as the utility of the (altruistic) parent. On the other hand, given fixed and variable costs for different levels of education, schooling lowers household income, and thus consumption. The scale of the benefits from education depends on school quality and child ability. The fraction of the pop- ulation falling in 17 different skill-types is endogenized by taking into account household schooling decisions in each period. The model has nonetheless two important limitations in relation to poverty analysis. First, household groups are only differentiated by their level of human capital (parents’ and child’s combined), which implies that it is impossible to see if some unrelated (to education) characteristics of the households explain skill acquisition behavior and only aggregate poverty measures can be analyzed. Second, there is only one representative firm and thus only a single aggregate demand for labor – instead of sectoral labor demands differing according to the sectoral intensities in the different types of labor – influences the wage premium. This is an important weakness in that poverty effects are gen- erally driven primarily through the income channel, which is mediated by household factor endowments and sectoral factor demands.
2005). The atmospheric component (ECBilt) is a quasi-
geostrophic model at T21 spectral resolution ('5.6 ◦ in lati-
tude/longitude) with additional parametrizations for the non- geostrophic terms (Opsteegh et al., 1998). ECBilt has three vertical layers in which only the first contains humidity as a prognostic variable. Precipitation is computed from the precipitable water of the first layer and falls in the form of snow if the temperature falls below 0 ◦ C. The time step of in- tegration of ECBilt is 4 h. The oceanic component (CLIO) is a 3-D Oceanic General Circulation Model (Goosse and Fichefet, 1999) run on a rotated B-grid at approximately 3 ◦ × 3 ◦ (lat-lon) resolution. It has a free surface that al- lows the use of real freshwater fluxes, a parametrization of downsloping currents and a realistic bathymetry. CLIO also includes a dynamical-thermodynamical sea-ice component (Fichefet and Morales Maqueda, 1997, 1999) on the same grid. The interactive vegetation component used is VECODE (Brovkin et al., 1997), a simple dynamical model that com- putes two Plant Functional Types (PFT: trees and grass) and a dummy type (bare soil). The vegetation model is resolved on the atmospheric grid (hence at T21 resolution) and allows fractional allocation of PFTs in the same grid cell to account
Average 100.0 863.0 81.6 67.0 32.4 19.8 62.5
Source: EPM93. authors' calculations.
Several indicators are used for this descriptive analysis and will be used again for the analysis of the results. The three indicators of poverty depend on the definition of a poverty line. Following several analyses of poverty in Madagascar, we took the per capita "caloric" line which corresponds to the poverty line used at the national level and which amounts to 248.000 1993 Francs Malgaches. This threshold corresponds to a per capita income sufficient to buy a minimum basket of basic foodstuffs (representing a ration of 2.100 Kcal per day) and of non- food staples. The first indicator (P0) is that of the poverty rate. It corresponds to the share of the population living below the poverty line, but does not inform about the degree of poverty. The second indicator is that of poverty depth (P1), where the contribution of each individual to the aggregate indicator is larger the poorer this individual. The third indicator is the severity of the poverty (P2), which is sensitive to inequality among the poor. Regarding income distribution, only the Theil index was retained as an indicator of inequality, because of its properties. It is a decomposable indicator, which makes it possible to consider the respective contributions of within and between-group inequality to total inequality. According to these indicators and the chosen poverty line, 67.0% of the population is poor in Madagascar. The poverty rate is higher in the rural sector where it reaches 74.9% of the population. The depth and severity of poverty are also higher in the rural sector. On the other hand, inequality is higher in the urban sector. Although the average income of the urban households is 2.7 times higher than that of the rural households, the between-group inequality accounts for only 15% of the overall inequality.
A well-known challenge in computable generalequilibrium (CGE) models is to maintain correspondence between the forecasted economic and physical quantities over time. Maintaining such a correspondence is necessary to understand how economic forecasts reflect, and are constrained by, relationships within the underlying physical system. This work develops a method for projecting global demand for passenger vehicle transport, retaining supplemental physical accounting for vehicle stock, fuel use, and greenhouse gas (GHG) emissions. This method is implemented in the MIT Emissions Prediction and Policy Analysis Version 5 (EPPA5) model and includes several advances over previous approaches. First, the relationship between per-capita income and demand for passenger vehicle transport services (in vehicle-miles traveled, or VMT) is based on econometric data and modeled using quasi-homothetic preferences. Second, the passenger vehicle transport sector is structured to capture opportunities to reduce fleet-level gasoline use through the application of vehicle efficiency or alternative fuel vehicle technologies, introduction of alternative fuels, or reduction in demand for VMT. Third, alternative fuel vehicles (AFVs) are introduced into the EPPA model. Fixed costs as well as learning effects that could affect the rate of AFV introduction are captured explicitly. This model development lays the foundation for assessing policies that differentiate based on vehicle age and efficiency, alter the relative prices of fuels, or focus on promoting specific advanced vehicle or fuel technologies.
The aim of this article is to analyze the potential for synergies between climate policies and development ina case study on India focusing on the power sector sub- optimalities. To do so, we use I MACLIM -R, a dynamic recursive energy-economy
model that represents asecondbest world with market imperfections and short-run adjustments constraints along a long-term growth path. The analysis suggests (i) global carbon pricing induces prohibitive macroeconomic costs for the Indian economy, even in the case of significant financial transfers associated with a global cap-and-trade system and a “Contraction and Convergence in 2100” allocation scheme; (ii) the most cost efficient climate policies are not uniform carbon pricing only. The implementation of domestic policies suited to the national context, for instance targeting sub-optimalities in the power sector for India, allows reducing significantly the macroeconomic costs induced by international mitigation policies.
We develop an endogenous growth modelin which energy services can be produced from a polluting non-renewable resource as well as a clean backstop. Moreover, we assume that carbon emissions can be partially released by using a CCS (Carbon Capture and Storage) technology. As formulated by Hoffert et al. (2002), the decarbonization, i.e. the reduction of the carbon content of each fossil fuel unit, i.e. the amount of carbon emitted per unit of primary energy, is intimately linked to sequestration. Carbon capture, sometimes referred to as emissions control (see Kolstad and Toman, 2001), is the way of achieving this decarbonization. This process consists in separating the carbon dioxide from other flux gases during the process of energy production. It is particularly adapted to large-scale centralized power stations but may also indirectly apply to non electric energy supply. Once captured, the gases are then being disposed into various reservoirs. The sequestration reservoirs include depleted oil and gas fields, depleted coal mines, deep saline aquifers, oceans, trees and soils. Those various deposits differ in their respective capacities, their costs of access or their effectiveness in storing the carbon permanently. In this respect, the introduction of some atmospheric pollution cap reinforces i) the recourse to CCS option in the middle run to prevent ceiling exceeding and ii) the necessity to subsidy research to improve CCS efficiency.
For the sake of simplicity, we assume common assumptions on all the technical and behavioral determinants of energy-economy trajectories, but sensitivity of model outcomes to these assumptions are studied in other specific papers 16 . The only variant concerns oil price trajectories, about which the literature on the strategic response of OPEC to various profiles of carbon prices often concludes that major oil producers would adopt a limited deployment of production capacities (IPCC, 2001, section 184.108.40.206). The rationale behind such behavior is to cut back production to trigger price increases and hence maintain revenues despite the drop of oil consumption (e.g., Berg and al., 1997b). However, low short-term oil prices may also have some advantages inaclimate policy context by simultaneously accelerating short-term oil consumption and limiting the incentive for oil-free technical change to sustain long-term oil demand. This is why we consider two oil pricing trajectories mimicking alternative reactions of Middle-East producers:
water resources and ultimately on water stress. The framework will also support the development of feedbacks to assess the implications of water stress on the economy.
This model also represents a significant improvement compared to global water models. First, by focusing on the U.S. we take advantage of water-use data detailed at the county level to estimate and project public supply, self-supply and mining water requirements. Additionally, the WRS-US model includes regional estimates of water for thermoelectric cooling, which are derived from the U.S. specific computable generalequilibriummodel (USREP). This application also takes advantage of U.S. farm survey data to precisely calibrate irrigation demand. The spatial disaggregation allows the detection of local water issues, such as the water deficit in the West. Future applications could focus on the impact of such water stress on economic activities. Such applications range from investigating water stress impacts on food production, to stream flow level impacts on naval transportation. This downscaled model also lays the foundations for further investigation of water allocation strategies (e.g., a comparative study of different
diﬀerent estimates of the elasticity of substitution to be obtained with diﬀerent ones. We need not assume changing preferences to rationalize this diﬀerence. It is suﬃcient to assume that elasticity of substitution across varieties varies with the consumption level.
The foregoing analysis has been developed in the case of homogeneous firms. Yet, there is mounting evidence that firms are heterogeneous in terms of productivity (see, e.g. Bernard et al., 2003). It is, therefore, natural to ask whether our modeling strategy can cope with heterogeneous firms à la Melitz (2003). This is what we accomplish in Section 4 where it is shown that both the cutoﬀ cost and markup decrease (increase) with the size of the market when the relative love for variety increases (decreases) with individual consumption. Under the same circumstances, the aggregate productivity rises or falls. All of this is to be contrasted with the CES where the market size has no impact on these variables. We thus find it fair to say that the distinction between the pro- and anti-competitive cases made above for homogeneous firms keeps its relevance when firms are heterogeneous. In addition, the results are obtained for ageneral distribution of marginal costs. Last, the nature of our main results still holds ina unified framework that explicitly combines vertical, horizontal and cost heterogeneity.
constraints, to derive the ecient production frontier and dene pro- ductivity growth as the outward shift of that eciency frontier rather than changes in observed input-output ratios.
Our model oers some explanation to productivity growth. Some in- puts can earn high returns if they are in short supply. TFP-growth is nothing but a re
ection of the evolution of marginal valuations of pri- mary factor inputs. The modeling of existing constraints is very crucial in our approach. Our computed aggregate TFP-growth rates are to a large extent explained by the bottelneck in construction. Perhaps the hypothesis of putty-clay capital in that low capital-intensive sector was overly restrictive. Another key role in our analysis is played by the levels of capacity utilization. Their construction is still controversial. No esti- mates are available for services. Proper measures of output and capacity utilization for services are problematic, but we urge Statistics Canada to devote resources to construct such measures. Our analysis would also be enriched if we could have data on sectoral use and total availability of la- bor disaggregated by level of qualication and of sectoral utilization and availability of capital disaggregated by type of capital. It would allow us to get a more precise picture of scarcities in the Canadian economy. By construction, the vintage structure of capital does not matter. To relax this assumption, we would need to make investment endogenous and switch to a dynamic model, which would lead to Hulten's notion of a dynamic residual.
Attracting investment ina timely manner will thus be essential and underlying assumption of the WEO 2007 scenario is that investment will be available and that this power infrastructure will be built in time, even if many challenges remain. Chronic underinvestment in power sector has been a major constraint to the country's development. The capacity addition targets set in the five-year plans have generally not been met and performance has deteriorated over the past three plans. Performance in the 10th plan period (2002-2007) was the worst ever. Less than half of the capacity envisaged was built. Insufficient investment resulted in electricity generation increasing at a rate well below the economic growth rate for five consecutive years (2001 to 2006), a situation never seen in the past. The pace of capacity additions stagnated in the 1990s. Economic reforms were introduced in 1991, in the expectation that part of the required investment would come from the private sector. But many of the projects proposed have not proceeded, mainly because of an inadequate legal and commercial framework, involving lack of law and contract enforcement and delays in obtaining regulatory approvals. The target in the 2007-2012 Plan foresees capacity additions of 69 GW, much higher than the unmet target of 41 GW set in the 10th Plan.