Economic Growth Scenarios Research Articles

Regional allocation of CO2 emissions allowance over provinces in China by 2020

The mitigation efforts of China are increasingly important for meeting global climate target since the rapid economic growth of China has led to an increasing share in the world's total CO2 emissions. This paper sets out to explore the approach for realizing China's national mitigation targets submitted to the UNFCCC as part of the Copenhagen Accord; that is, to reduce the intensity of CO2 emissions per unit of GDP by 40–45% by 2020, as well as reducing the energy intensity and increasing the share of non-fossil fuel consumption, through regional allocation of emission allowance over China's provinces. Since the realization of China's mitigation target essentially represents a total amount emission allowance allocation problem, an improved zero sum gains data envelopment analysis optimization model, which could deal with the constant total amount resources allocation, is proposed in this study. By utilizing this model and based on several scenarios of China's economic growth, CO2 emissions, and energy consumption, a new efficient emission allowance allocation scheme on provincial level for China by 2020 is proposed. The allocation results indicate that different provinces have to shoulder different mitigation burdens in terms of emission intensity reduction, energy intensity reduction, and share of non-fossil fuels increase.

Improving the assessment and valuation of climate change impacts for policy and regulatory analysis

The social cost of carbon (SCC) is a monetized metric for evaluating the benefits associated with marginal reductions in carbon dioxide (CO2) emissions. It represents the expected welfare loss from the future damages caused by the release of one tonne of CO2 in a given year, expressed in consumption equivalent terms. It is intended to be a comprehensive measure, taking into account changes in agricultural productivity, human health risks, loss of ecosystem services and biodiversity, and the frequency and severity of flooding and storms, among other possible impacts. Estimating the SCC requires long-term modeling of global economic activity, the climate system, and the linkages between the two through anthropogenic greenhouse gas (GHG) emissions and the effects of changing climatic conditions on economic activity and human well-being. The United States government currently uses the SCC in regulatory benefit-cost analyses to assess the welfare effects of changes in CO2 emissions. Consistent application of the SCC to federal rulemaking analyses began in 2009-2010 with the development of a set of global SCC estimates that employed three prominent integrated assessment models (IAMs) -- DICE, FUND, and PAGE. The U.S. government report identified a number of limitations associated with SCC estimates in general and itsmore » own assumptions in particular: an incomplete treatment of damages, including potential “catastrophic” impacts; uncertainty regarding the extrapolation of damage functions to high temperatures; incomplete treatment of adaptation and technological change; and the evaluation of uncertain outcomes in a risk-neutral fashion. External experts have identified other potential issues, including how best to model long-term socio-economic and emissions pathways, oversimplified physical climate and carbon cycle modeling within the IAMs, and an inconsistency between non-constant economic growth scenarios and constant discount rates. The U.S. government has committed to updating the estimates regularly as modeling capabilities and scientific and economic knowledge improves. To help foster further improvements in estimating the SCC, the U.S. Environmental Protection Agency and the U.S. Department of Energy hosted a pair of workshops on “Improving the Assessment and Valuation of Climate Change Impacts for Policy and Regulatory Analysis.” The first focused on conceptual and methodological issues related to integrated assessment modeling and the second brought together natural and social scientists to explore methods for improving damage assessment for multiple sectors. These two workshops provide the basis for the 13 papers in this special issue.« less

Southeast Asia. The institutional imperative: The politics of equitable development in Southeast Asia. By Erik Martinez Kuhonta. Stanford: Stanford University Press, 2011. Pp. 342. Maps, Illustrations, Notes, Bibliography, Index.

Southeast Asia. The institutional imperative: The politics of equitable development in Southeast Asia. By Erik Martinez Kuhonta. Stanford: Stanford University Press, 2011. Pp. 342. Maps, Illustrations, Notes, Bibliography, Index. - Volume 43 Issue 3

A system dynamics approach for urban water reuse planning: a case study from the Great Lakes region

Water reclamation and reuse practices are recently receiving growing attention due to increasing water scarcity, concerns about the effect of wastewater discharges on receiving water, and availability of high-performing and cost-effective water reuse technologies. However, incorporation of water reuse schemes into water/wastewater infrastructure systems is a complex decision making process, involving various economical, technological, and environmental criteria. System dynamics (SD) allows modeling of complex systems and provides information about the temporal and feedback behavior of the system. In this sense, a SD model of the existing water/wastewater system in Kalamazoo-Michigan, an urban area in the Great Lakes region, was created with the hypothetical incorporation of water reuse. The model simulates and optimizes the overall water system cost (including water, wastewater and water reuse components), accounting for future scenarios of population, economic growth and climate change. Results indicate significant levels of water reuse after an infrastructure build delay. The model also indicates that a decision to implement water reuse yields remarkably lower water withdrawals and lower water treatment costs even in a location with a relatively abundant water supply like Kalamazoo. This study emphasizes the fact that a true understanding of the practice of water reuse cannot be achieved without taking regional and climatic parameters into account.

Downscaling global income scenarios assuming institutional convergence or divergence

This paper offers a new approach to generating long-term global scenarios of economic growth at national level. The paper presents a simple model grounded in the economic theory of conditional convergence. The free parameters in the model, which characterize different scenarios, reflect rates of institutional and structural convergence between countries. The model can be used either for generating new global economic scenarios, or for downscaling existing regional economic scenarios.

Designing a distribution network with uncertainty demands and costs

This paper addresses a distribution network design problem in a 3-echelon supply chain subjected to uncertainty in the input data at strategic planning. It uses a basic model formulated as mixed-integer programming (MIP) that involves in locating production plants and distribution warehouse, and determines the best strategy for distributing the product from plants to the warehouse and from the warehouse to customers. These strategic decisions affect the efficiency and effectiveness of the designed system over the implementation time; thus, it seems necessary for the decision makers to build a real-world model at this level of planning. The study is faced with the uncertainty in many decision parameters, such as customer demands and costs. Thus, this study uses two uncertain problem-solving approaches, namely robust optimization and interval linear programming, for the basic model presented by Amiri (2006). The first approach generates a series of solutions that are progressively less sensitive to realization of the input data from a set of scenarios. Its main goal is to select the optimum numbers of locations and capacities of plants and warehouses, so that the total cost of the distribution network is minimized under different economic growth scenarios. Furthermore, this study uses interval linear programming to consider uncertainty in the distribution network. Thus, the basic MIP model is now developed into two models, namely robust optimization and interval linear programming). Finally, the Amiri’s model is also solved by the optimization software along with the two forgoing developed models. To show their efficiency, the related results are compared and illustrated. It is concluded that the results obtained by robust optimization are superior to the results reported by interval linear programming and the Amiri’s model. Key words: Distribution network, supply chain, uncertainty, robust optimization, interval linear programming.

Potential Trade-Offs Between Future Economic Growth and Open Land Conservation Adjacent to Public Protected Areas: A Case Study in Northwest Montana

Potential trade-offs between future economic growth and the conservation of open land adjacent to public protected areas in Flathead County in Northwest Montana are evaluated for nine economic growth–land use policy futures. Future increases in total output and total jobs under three economic growth scenarios are projected using the IMPLAN model. The locations of parcels developed between 2005 and 2024 under the nine futures are simulated using the Ecosystem Landscape Modeling System. Open land adjacent to public protected areas in Flathead County decreases as economic growth and associated land development increase. Implementing a more restrictive land use policy may reduce but not eliminate the loss of open land from economic growth and associated land development. Trade-offs between total output and open land conservation and between total jobs and open land conservation decrease as economic growth decreases and/or the land use policy becomes more restrictive.

China's Energy Demand Scenario Analysis in 2030

In order to forecast energy demand in China in the next 20 years, this paper firstly analyzes the current situation of China's energy consumption, and then apply LEAP model to simulate primary energy and final energy demand in 2020, 2030 under different scenario composition about economic development, energy efficiency and energy structure. The results show that: the total primary energy demand will reach 4840-5070 Mtce in 2020, 5580-5870 Mtce in 2030; the share of coal in primary energy will decrease, and that of oil, natural gas and non-fossil energy sources will increase. For all the three economic growth scenarios, 40-45% carbon emission intensity reduction target can be realized. If non-fossil energy can be further developed, carbon emission intensity can reduce more than 45% by 2020. The goal of the 15% share of non-fuel energy in primary energy is difficult to realize, unless the most development potentials of hydro power, nuclear power and wind power can be reached.

Study on the mid-long term electrification level of China

Electrification level is a significant topic in energy and electric power planning. The paper sets up a LEAP model and designs three scenarios of economic growth of China, and simulates China's electrification level by 2030. The results show that China's electrification level has a larger increase potential in the mid-long term and rapid change of economic development pattern can help to increase the electrification level. By 2015, energy used for power generation will account for 43.3% of total primary energy consumption, while the ratio of electricity consumption to total final energy consumption will be about 23.1%; by 2020, the ratio of energy used for power generation to total primary energy consumption will increase to 44.9%, and the ratio of electricity consumption to final energy consumption will be about 25.6%; by 2030, the ratio of energy used for power generation to total primary energy consumption will be up to 47.2%, and the ratio of electricity consumption to total final energy consumption will reach about 28.9%.

If not growth, then what?

This paper examines the misalignment between growth expectations of economic models and the finite capacity of the planet to sustain such growth. We utilise public data to illustrate that resources required to sustain human life are being degraded to a point that should warrant collective action. In examining a range of economic growth scenarios, we posit a growth–equity frontier that constrains human kind's choices, and caution that discussions around growth rates require a fundamental shift in how human kind views economic success, productivity and human happiness.

Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world

The uncertainty associated with predicting extreme weather events has serious implications for the developing world, owing to the greater societal vulnerability to such events. Continual exposure to unanticipated extreme events is a contributing factor for the descent into perpetual and structural rural poverty. We provide two examples of how probabilistic environmental prediction of extreme weather events can support dynamic adaptation. In the current climate era, we describe how short-term flood forecasts have been developed and implemented in Bangladesh. Forecasts of impending floods with horizons of 10 days are used to change agricultural practices and planning, store food and household items and evacuate those in peril. For the first time in Bangladesh, floods were anticipated in 2007 and 2008, with broad actions taking place in advance of the floods, grossing agricultural and household savings measured in units of annual income. We argue that probabilistic environmental forecasts disseminated to an informed user community can reduce poverty caused by exposure to unanticipated extreme events. Second, it is also realized that not all decisions in the future can be made at the village level and that grand plans for water resource management require extensive planning and funding. Based on imperfect models and scenarios of economic and population growth, we further suggest that flood frequency and intensity will increase in the Ganges, Brahmaputra and Yangtze catchments as greenhouse-gas concentrations increase. However, irrespective of the climate-change scenario chosen, the availability of fresh water in the latter half of the twenty-first century seems to be dominated by population increases that far outweigh climate-change effects. Paradoxically, fresh water availability may become more critical if there is no climate change.

Estimating the climate and air quality benefits of aviation fuel and emissions reductions

Abstract In this study we consider the implications of our current understanding of aviation climate impacts as it relates to the ratio of non-CO 2 to CO 2 effects from aviation. We take as inputs recent estimates from the literature of the magnitude of the component aviation impacts and associated uncertainties. We then employ a simplified probabilistic impulse response function model for the climate and a range of damage functions to estimate the ratio of non-CO 2 to CO 2 impacts of aviation for a range of different metrics, scientific assumptions, future background emissions scenarios, economic growth scenarios, and discount rates. We take cost-benefit analysis as our primary context and thus focus on integral metrics that can be related to damages: the global warming potential, the time-integrated change in surface temperature, and the net present value of damages. We also present results based on an endpoint metric, the global temperature change potential. These latter results would be more appropriate for use in a cost-effectiveness framework (e.g., with a well-defined policy target for the anthropogenic change in surface temperature at a specified time in the future). We find that the parameter that most influences the ratio of non-CO 2 to CO 2 impacts of aviation is the discount rate, or analogously the time window used for physical metrics; both are expressions of the relative importance of long-lived versus short-lived impacts. Second to this is the influence of the radiative forcing values that are assumed for aviation-induced cloudiness effects. Given the large uncertainties in short-lived effects from aviation, and the dominating influence of discounting or time-windowing, we find that the choice of metric is relatively less influential. We express the ratios of non-CO 2 to CO 2 impacts on a per unit fuel burn basis so that they can be multiplied by a social cost of carbon to estimate the additional benefits of fuel burn reductions from aviation beyond those associated with CO 2 alone (all else being equal). For a non-CO 2 to CO 2 ratio based on economic damage costs, we find a central value of 1.8 at a 3% discount rate, with a range from 0.6 to 2.5 for the upper and lower bounds of scientific and scenario-based uncertainty. Since estimating the co-benefits in this way is an important requirement for cost-benefit analyses, we also provide estimates of the air quality benefits of aviation fuel burn reduction in a similar format. We find the marginal damage costs of aircraft emissions below 3000 feet to be of similar magnitude to the climate costs on a per unit fuel burn basis, or an order of magnitude smaller on a per flight basis since we take no account of the air quality impacts of emissions above 3000 feet where the majority of fuel is consumed for the fleet.

Teaching the Solow Through the Building of Economic Growth Scenarios: An Application to China

In this article, I present a pedagogical strategy for teaching the Solow’s economic growth model in a more “learnable” way. In particular, I develop a learning strategy through the building of economic growth scenarios by “running” widely available economic information in a pre-formatted Excel spreadsheet. This strategy recovers previous pedagogical devices in teaching the Solow’s model and follows seminal approach developed by Denison (1962). This exercise is planned to be useful for intermediate or MBA macroeconomics students. In this presentation, I apply this procedure for the China’s economy.

Development of flood exposure in the Netherlands during the 20th and 21st century

Flood risks of deltaic areas increase because of population growth, economic development, land subsidence and climatic changes such as sea-level rise. In this study, we analyze trends in flood exposure by combining spatially explicit historical, present, and future land-use data with detailed information on the maximum flood inundation in the Netherlands. We show that the total amount of urban area that can potentially become inundated due to floods from the sea or main rivers has increased six-fold during the 20th century, and may double again during the 21st century. Moreover, these developments took, and probably will take, place in areas with progressively higher potential inundation depths. Potential flood damage has increased exponentially over the 20th century (16 times) and is expected to continue to increase exponentially (∼ten-fold by 2100 with respect to 2000) assuming a high economic growth scenario. Flood damages increase more moderately (two- to three-fold by 2100 with respect to 2000) assuming a low growth scenario. The capacity to deal with catastrophic flood losses – expressed as the ratio damage/GDP – will, however, decrease slightly in the low growth scenario (by about 20%). This trend deviates from the historical trend of the 20th century, which shows an increasing capacity to cope with flood damage (almost doubling). Under the high growth scenario the capacity to deal with such losses eventually increases slightly (by about 25%). These findings illustrate that, despite higher projections of potential flood damage, high economic growth scenarios may not necessarily be worse than low growth scenarios in terms of the impact of floods.

A Welfare Model for a Global Carbon Market Under Uncertain Information

In the transition to a sustainable global economy, market organization is challenged by the recent financial and economic crises. However, at the instance of the European Union’s Emission Trading Scheme (EU ETS) [2] accounting for 66% of all dioxide of carbon (CO2) traded worldwide - major players recognise the role of market mechanisms in the fight against climate change. This paper presents a Welfare model in the context of a global carbon market with uncertain information. The objective is to analyse whether a global carbon market could achieve a fair distribution of resources and risks in the framework of four sets of energy and climate scenarios at 2030 and 2100 time horizons: A1 scenarios of rapid economic growth and efficient technology spread worldwide; A2 scenarios of slower and more fragmented technological changes and improvements of per capita income with a regionally-oriented economic development; B1 scenarios of a more integrated and more ecologically friendly world; B2 scenarios of a more divided, but more ecologically friendly world.The agents in the model are households, firms, and a public institution. In the long run it is assumed that the allocated quotas diminish, and the price increases strongly. However, this process may not be linear and the public institution in charge of the carbon policy may have to adjust the reduction path when it receives new information about the climate risk and the investment requirements.This paper highlights the need to move away from the current barter system to a global exchange system, and shows how such an exchange system could achieve a fair welfare distribution between firms, households and an independent public institution. When the market is not constrained Pareto efficient, an active and dynamic carbon policy can make all players better-off. In this framework the carbon market is likely to be a key driving force of future energy policies and strategies.

Climate-change feedback on economic growth: explorations with a dynamic general equilibrium model

ABSTRACTHuman-generated greenhouse gases depend on the level and emissions intensity of economic activities. Therefore, most climate-change studies are based on the models and scenarios of economic growth. Economic growth itself, however, is likely to be affected by climate-change impacts. These impacts affect the economy in multiple and complex ways: changes in productivity, resource endowments, production and consumption patterns. We use a new dynamic, multi-regional computable general equilibrium (CGE) model of the world economy to answer the following questions: Will climate-change impacts significantly affect growth and wealth distribution in the world? Should forecasts of human-induced greenhouse gas emissions be revised, once the climate-change impacts are taken into account? We found that, even though economic growth and emission paths do not change significantly at the global level, relevant differences exist at the regional and sectoral level. In particular, developing countries appear to suffer the most from the climate-change impacts.

Simulation on the dynamics of forest area changes in Northeast China

There is plenty of forests in Northeast China which contributes a lot to the conservation of water and land resources, produces timber products, and provides habitats for a huge number of wild animals and plants. With changes of socio-economic factors as well as the geophysical conditions, there are dramatic changes on the spatial patterns of forest area. In this sense, it is of great significance to shed light on the dynamics of forest area changes to find the underlining reasons for shaping the changing patterns of forest area in Northeast China. To explore the dynamics of forest area change in Northeast China, an econometric model is developed which is composed of three equations identifying forestry production, conversion from open forest to closed forest and conversion from other land uses to closed forest so as to explore the impacts on the forest area changes from demographic, social, economic, location and geophysical factors. On this basis, we employ the Dynamics of Land System (DLS) model to simulate land-use conversions between forest area and non-forest cover and the land-use conversions within the sub-classes of forest area for the period 2000-2020 under business as usual scenario, environmental protection scenario and economic growth scenario. The simulation results show that forest area will expand continuously and there exist various kinds of changing patterns for the sub-classes of forest area, for example, closed forest will expand continuously and open forest and shrub will decrease a little bit, while area of other forest will keep intact. The research results provide meaningful decision-making information for conserving and exploiting the forest resources and making out the planning for forestry production in the Northeast China region.

Accounting for carbon stocks in models of land-use change: an application to Southern Yucatan

To assess the impact of land-use change on carbon stocks, we apply a new methodology, linking ecological and economic modeling, to southern Yucatan, Mexico. A spatial econometric multinomial logit model of ten land-cover classes is estimated (four primary forest categories, three secondary growth categories, an invasive species, and two agricultural land-cover categories), using satellite data on land cover, linked with census socioeconomic data and other biophysical spatial data from 2000. The analysis is novel in that it is the first attempt to link detailed satellite data on land use, with on-the-ground estimates of carbon stocks in a spatial econometric model of land use. The estimated multinomial logit model is then used with two scenarios of future economic growth (“low growth” and “high growth” changes in population, agricultural land use, market access, and education levels) in the region to predict land-cover changes resulting from the economic growth. The per hectare carbon (C) stocks in each land-cover class are derived from previously published estimates of biomass from field sampling across the study region. We consider aboveground-only, aboveground plus soil, transient and non-transient pools of carbon. These estimates are scaled up to the total area in each class according to the predictions of the model baseline and the two development scenarios. Subsequently, the changes in carbon stocks resulting from the predicted land-cover changes are calculated. Under the low growth scenario, carbon stocks declined by 5%; under the high growth scenario, losses were 12%. Including soil C, the proportional losses were lower, but the absolute amount lost was more than double (to 6 Tg C under the low and almost 15 Tg C under the high-growth scenario). This methodology could be further developed for applications in global change policy, such as payments for environmental services (PES) or reduction in emissions from deforestation and degradation (REDD).

Climate Change Feedback on Economic Growth: Explorations with a Dynamic General Equilibrium Model

Human-generated greenhouse gases depend on the level of economic activity. Therefore, most climate change studies are based on models and scenarios of economic growth. Economic growth itself, however, is likely to be affected by climate change impacts. These impacts affect the economy in multiple and complex ways: changes in productivity, resource endowments, production and consumption patterns. We use a new dynamic, multi-regional Computable General Equilibrium (CGE) model of the world economy to answer the following questions: Will climate change impacts significantly affect growth and wealth distribution in the world? Should forecasts of human-induced greenhouse gases emissions be revised, once climate change impacts are taken into account? We found that, even though economic growth and emission paths do not change significantly at the global level, relevant differences exist at the regional and sectoral level. In particular, developing countries appear to suffer the most from climate change impacts.

Urbanization and fertility rates in Ethiopia

Fertility rates are important determinants of both overall population growth and demographic transitions from high to low age dependency ratios, which in turn have important consequences for economic growth, poverty reduction, and improved health and nutrition outcomes. Ethiopia currently has one of the highest fertility rates in the world, although there are marked differences between rural and urban fertility rates. This paper explores the drivers of rural and urban fertility rates, including systematic tests of differences in key determinants. This further allows us to project fertility rates into the future based on alternative urbanization, economic growth, and education scenarios. Finally, we link these alternative projections with existing estimates of the benefits of fertility reductions on economic growth, nutrition, and poverty reduction