Channing Arndt

Climate uncertainty and economic development: evaluating the case of Mozambique to 2050

We apply a probabilistic approach to the evaluation of climate change impacts in Mozambique. We pass a distribution of climate shocks through a series of structural biophysical models. The resulting joint distributions of biophysical outcomes are then imposed on a dynamic economywide model. This framework produces distributions of economic impacts of climate change thus identifying an explicit range of potential economic outcomes and associating probability levels with given sets of outcomes. For example, we find that the economy of Mozambique may be up to 13xa0% smaller in 2050 compared with a fictional no climate change scenario (and assuming global policy fails to constrain emissions growth). The probability of gross domestic product (GDP) declines of greater than 10xa0% is relatively small at 2.5xa0%. These large declines are principally the result of dramatic reductions in flood return periods. To 2050, about 70xa0% of future climates result in GDP losses of between zero and five percent. In about 9xa0% of cases, climate change shocks result in higher GDP outcomes. We conclude that, relative to current practice, this structural probabilistic approach provides (i) significantly more information to decision-makers, (ii) more detailed insight into the importance of various impact channels, and (iii) a more holistic and comprehensive approach for evaluating adaptation options.

Informed Selection of Future Climates

Analysis of climate change is often computationally burdensome. Here, we present an approach for intelligently selecting a sample of climates from a population of 6800 climates designed to represent the full distribution of likely climate outcomes out to 2050 for the Zambeze River Valley. Philosophically, our approach draws upon information theory. Technically, our approach draws upon the numerical integration literature and recent applications of Gaussian quadrature sampling. In our approach, future climates in the Zambeze River Valley are summarized in 12 variables. Weighted Gaussian quadrature samples containing approximately 400 climates are then obtained using the information from these 12 variables. Specifically, the moments of the 12 summary variables in the samples, out to order three, are obliged to equal (or be close to) the moments of the population of 6800 climates. Runoff in the Zambeze River Valley is then estimated for 2026 to 2050 using the CliRun model for all 6800 climates. It is then straightforward to compare the properties of various subsamples. Based on a root of mean square error (RMSE) criteria, the Gaussian quadrature samples substantially outperform random samples of the same size in the prediction of annual average runoff from 2026 to 2050. Relative to random samples, Gaussian quadrature samples tend to perform best when climate change effects are stronger. We conclude that, when properly employed, Gaussian quadrature samples provide an efficient and tractable way to treat climate uncertainty in biophysical and economic models.

Chapter 14 Aid and Development: The Mozambican Case

We consider the relationship between external aid and development in Mozambique from 1980–2004, identifying the specific mechanisms through which aid has influenced the developmental trajectory of the country. We undertake both a growth accounting analysis and review the intended and unintended effects of aid at the micro-level. Sustained aid flows to Mozambique, in conflict and post-conflict periods, have made an unambiguous, positive contribution to rapid growth since 1992. However, proliferation of donors and aid-supported interventions has burdened local administration, indicating a need for deeper domestic government accountability. To sustain growth, Mozambique must maximize benefits from natural resources while promoting constructive international market integration.

Climate change impacts and adaptations: lessons learned from the greater Zambeze River Valley and beyond

In this article, we assert that developing countries are much better prepared to undertake negotiations at the Conference of the Parties in Paris (CoP21) as compared to CoP15 in Copenhagen. An important element of this is the accumulation of knowledge with respect to the implications of climate change and the ongoing internalization thereof by key institutions in developing countries. The articles in this special issue set forth a set of technical contributions to this improved understanding. We also summarize five major lessons related to uncertainty, extreme events, timing of impacts, the inseparability of the development and climate agendas, and the rate of assimilation of climate and development information in key institutions. They are drawn from the Development Under Climate Change (DUCC) project carried out by UNU-WIDER of which the countries of the Greater Zambeze Valley formed a part. Finally, we outline three areas for future research.

Inequality and Poverty Impacts of Trade Distortions in Mozambique

Although Mozambique has considerable agricultural potential, rural poverty remains extremely high. This paper examines the extent to which global and domestic price distortions affect agricultural production and national poverty. The author develops a computable general equilibrium (CGE) and micro-simulation model of Mozambique that is linked to the results of a global model. This framework is used to examine the effects of eliminating global and national price distortions. Model results indicate that agriculture is adversely affected by current trade distortions due to policies in the rest of the world. While a removal of all merchandise trade distortions will reduce import prices, it will also raise agricultural production and reduce poverty. By contrast, removing only agricultural price distortions abroad will have little effect on Mozambiques agricultural sector. Model results indicate that Mozambiques own distortions are also biased against agriculture, with producers of processed agricultural products enjoying high protection levels. Removing these distortions causes a significant expansion of agricultural Gross Domestic Product (GDP) and a reduction in both poverty and inequality. The findings therefore suggest that removing own-country and rest-of-world distortions will have positive implications for agriculture and for the overall economy in Mozambique, and in particular it will reduce its poverty and inequality.