Importance Of Estimating Calculating Costs Of Adaptation

Published Date: 02 Nov 2017

There is general scientific consensus that the world is experiencing climate change and that there are increasing frequency and severity of natural disasters (Stern 2008; UNDP 2007; World Bank 2010).

The fourth assessment report (FAR) of the Intergovernmental Panel on Climate Change (IPCC) (Pachauri and Reisinger 2007) reports that even with the present climate change mitigation policies and related sustainable development practices, the global greenhouse gas (GHG) emissions will continue to grow over the first to decades. The projected increase between 2000 and 2030 is estimated to be between 25-90% (Nakićenović, et al. 2000) Table 1.1 shows us the projected global average surface warming and sea level rise at the end of the 21st century. It can be seen that even with constant concentrations of the year 2000 there would be an increase in temperature in the range of 0.3 to 0.9 °C. It is also estimated that the increase in average surface temperature would continue and lead to an increase of 1.1 to 6.4 °C (Pachauri and Reisinger 2007). While the long term impacts of climate change can be countered by reducing emission of greenhouse gases, it has also now been understood now that the climate change cannot be altogether avoided as hard as we may try to because of the inertia of the global climate system (Parry et al. 1998). There would be variation in the warming but this would be accompanied by significant changes in precipitation patterns, frequency and intensity of extreme events. There is also a projected rise of the global sea levels from .18 to about .59 m 2100. This would have significant implications for 50-70% of the world‘s population that live in the coastal areas.

Table 1. Projected global average surface warming and sea level rise at the end of the 21st century

Case

Temperature change

(°C at 2090-2099 relative to 1980-1999)

Sea level rise

(m at 2090-2099 relative to 1980-1999)

Constant year 2000 concentrations

0.3-0.9

Not available

B1 [1] scenario

1.1-2.9

0.18-0.38

A1T scenario

1.4-3.8

0.20-0.45

B2 scenario

1.4-3.8

0.20-043

A1B scenario

1.74-4.4

0.21-0.48

A2 scenario

2.0-5.4

0.23-0.51

A1FI scenario

2.4-6.4

0.26-0.59

Source: (Parry, Canziani, et al. 2007)

Thus whether or not there would be an agreement on a strict global mitigation policy, every country will have to answer the question on how to adapt to the changes in climate in the future. Adaptation would be important to prevent or reduce the negative impacts that anthropogenic climate change would have on the various aspects of life. Nevertheless, national and international climate policies were largely focused on mitigation until recently. There was also a very limited understanding of what adaptation consists of largely due to the limited research conducted in this field. It is after the IPCC third assessment report (TAR) that it dawned that some impacts of climate change can no longer be prevented and there in now increased attention for adaptation in both academics and policy. However it has to be understood that the science of adaptation to climate change is still in a nascent stage and there is a need for research in various aspects of adaptation and incorporate learning from different areas like anthropology and ecology (Klein et al 2005)

Importance of estimating/calculating Costs of Adaptation

With the understanding that at least some level of adaptation cannot be avoided, there are an increasing number of countries, both developed and developing, which have started formulation and implementation of adaptation activities. There are various challenges for decision makers to decide options and strategies while addressing adaptation. There is a need for robust and transparent assessment and decision making frameworks so as to enable efficient allocation of resources. It is also necessary to assess the various risks posed by climate change and design national and local level adaptation strategies. Understanding the various options available and the related trade-offs are some aspects of decision making. The estimations of the various costs of adaptation national levels for different sectors are also important to plan the adaptation in an effective and efficient manner.

What is essential to note here is that the source of most of the emissions which have lead to global warming have been the Annex – 1 countries, which account to about 74% of the greenhouse gas emissions since 1850 and 65% of emissions since 1970. Countries like India and China together only account for 13% since 1850 and 16% since 1970.The other emerging economies account for just 9% and 12% since 1850 and 1970 respectively. But the problem of climate change has imposed a constraint on the development pathways of countries across the world. Apart from mitigation, almost all countries have to also deal with adaptation costs. Even though it is the developed countries that are primarily responsible for the climate change crisis, the developing and least developed countries have to bear the burden of the adaptation costs. These costs would be added on to the already existing development spending. In terms of international climate negotiations the cost of adaptations has become an important issue.

The Bali Action Plan which was adopted at the 2007 United Nations Climate change conference calls for allocation of "adequate, predictable, and sustainable financial resources and new and additional resources, including official and concessional funding for developing country parties" by developing countries. The need for international cooperation for capacity building to integrate the adaptation measures into the national and sectoral plans is also emphasised. Thus global and regional estimation of costs of adaptation is important in the context of climate negotiations.

Objectives and Methodology

The study was initially started with an objective on estimating costs of infrastructure adaptation for India for the next twenty to thirty years. The methodology adopted by the World Bank in it Economics of Adaptation to Climate Change (World Bank 2010) study was to be applied to the investments of the 12th Five year plan to come up with an indicative estimate of costs. On further investigations and literature review it was understood that this would be a highly ambitious project which would require more resources and expert inputs to be completed within the given time span. Thus it was decided to focus on costs of adaptation for roads.

Thus in this study there is a review of various studies which have been conducted on estimation of costs to gain an understanding of the methodologies that have been followed. These studies include the study by World Bank (2006), the Stern Review (2007), the Human Development Report 2007/2008 of the United Nations Development Programme (2008), the Oxfam (2007) study, the study by the United Nations Framework Convention on Climate Change (2007) and the Economics of Adaptation to Climate Change (EACC) study (World Bank 2010). The methodology of the EACC was applied to the 12th Five Year Plan investments in roads to come up with indicative costs of adaptation.

In addition to these global studies, there is a description of few regional studies which study the impacts of climate change on specific aspects of infrastructure and the costs of adaptation. This is to understand the possible methodologies which can be used to improve upon the methodology adopted in the EACC study and look at a more bottom up approach in assessment of impacts and estimation of costs. It is recommended that a more extensive and in depth study would be required to estimate costs of adaptation for all of India.

LITERATURE REVIEW

Economics of Adaptation to Climate Change

Economics is the study of allocation of scarce resources that have alternative uses which increases the relevance of trade-offs (between different uses of resource) and opportunity costs. In the context of climate change, this means that (i) costs and benefits matter; (ii) resources are not free; and (iii) resources used for one purpose are no longer available for other purposes (Bruce, Lee and Haites 1996). We will try to look at some concepts and methods of economics which can be applied to the assessment and decision making with respect to adaptation strategies (Bruce, Lee, and Haites 1996; Stern 2006)

Risk and Uncertainty

"The risks and uncertainties around the costs and benefits of climate policy are large; hence the analytical framework should be able to handle risk and uncertainty explicitly". (Stern 2007) The term risk is used when there is some sense of the probabilities and uncertainty when there is no ability to assess probabilities. Risk can be defined as the "the product of the probability of an event occurring and its severity" (Ranger, et al. 2009). The rational approaches to deal with risk have been studied and these could be used for a systematic understanding and approach to climate change. One of these is the portfolio theory (Bruce, Lee and Haites 1996) which looks at investment portfolios designed to the give the best returns for a given level of risk. Thus when faced with risk there will be responses that would either

Reduce the probability that the unfavourable event occurs

Reduce the cost in case the event occurs

Insure against the risk and spread

Similarly, in the context of climate change; nations can reduce the probability of global warming through reduction of greenhouse gas emissions, adapt to climate change if it does occur or insure against the risks. But it is also realised that these three cannot be mutually exclusive anymore given the extent and uncertainty of the impacts of climate change. A well-chosen portfolio of climate change investments will yield greater benefit for a given cost than any one option undertaken alone. Both mitigation and adaptation actions would be included in a portfolio approach For an individual country, the issue is how to choose the portfolio of policy measures best suited to its circumstances and to adjust the portfolio over time in response to new developments.

Uncertainty is "an expression of the degree to which a quantity (e.g., the future state of the climate system) is unknown." (Ranger, et al. 2009). Uncertainty can be a result of the lack of information or from disagreement about what is known or even knowable. "Uncertainty is a problem endemic to climate change" (World Bank 2009) There are increasing levels of uncertainty at every stage of understanding climate change, its extent and timing, the impacts on the ecosystem, economy and human beings, the assessment of costs and benefits of adaptation and how it would change over time. Figure 2.1 depicts this increasing uncertainty at every level. This makes it hard to formulate and implement an optimum strategy beforehand, since it becomes uncertain when and what actions have to be implemented.

Figure 2. The ‘explosion’ of uncertainty from global emissions to local impacts

Source: (Ranger, et al. 2009)

Sources of Uncertainty. Ranger, et al. (2009) have elaborated on the aleatory and epistemic uncertainty. Aleatory uncertainty stems from unpredictable natural variations like the chaotic nature of climate while epistemic uncertainty has its roots in the lack of knowledge about a system as exemplified by the uncertainties in modelling the response of regional climates to global greenhouse gas levels and in modelling the effects of warming on biological systems. The authors also say that while aleatory uncertainties can be quantified, they cannot be reduced but epistemic uncertainties can be quantified and reduced with an increased understanding and availability of information. Two more kinds of uncertainty comes in forecasting of human systems like demographics, economic growth, global emissions etc. which makes impact estimates highly sensitive to these uncertainties which are largely not possible to be reduced (Ranger, et al.. 2010). Finally, one of the well-studied sources of uncertainty is found in the projections of climate change. While the current models can provide some information about the future climate change they haven‘t been able to do so with a high degree of confidence. This is particularly evident at a local scale and with long prediction span of time.

Dealing with Uncertainty. Operationalization of adaptation costs requires dealing with the considerable uncertainty about future climate projections. This suggests that a range of adaptation costs should be estimated for a range of climate scenarios (World Bank 2010) Thus it can be understood that adaptation decisions are going to be taken under conditions of uncertainty. Ranger et al. argue that for long term decision making for adaptation, there is a need to improve the climate projections through continued investment, but it would be unlikely that there would be any significant reductions in uncertainty in the next five years. Hallegatte (2008) also suggests that improved models of climate change projection would not necessarily mean a more narrow projection range. In contrast at times the inclusion of previously missing process has actually increased the uncertainty range, though with a higher confidence. Thus for short term decisions on adaptation, it would be best to invest in research areas of more "reducible" uncertainties which would also have significant bearing on the decision (Dessai et al. 2009). Agrawala and Fankhauser contend that win-win [2] measure of adaptation that are justified even in the absence of climate change would be the best way forward while Fankhauser et al. (1999) have argued that given the prevailing uncertainties, the best way to account for potential climate change in current investment decisions may be to increase the flexibility and robustness of systems – allowing them to function under a wide range of climatic conditions and withstanding more severe climatic shocks (Agrawala & Fankhauser 2008).

Time Frame and Discounting

While conducting an economic assessment of adaptation, particularly for infrastructure, the choice of timeframe is an important factor. The reliability of models and predictions, uncertainty of potential forecasts of climate change and its impacts on economy, the lifespan of infrastructure projects etc. play an important role in determining the timeframe. Most studies have a time frame which ranges from 20 to 50 years in the future because of the aforesaid reasons.

It is discerned that the "discounting technique is relevant for marginal perturbations around a given growth path but not comparing across paths" (Stern 2006). The discount factor is the key concept here. It is the "value of an increment in consumption at a time in the future relative to now." (Stern 2006) This generally depends on the consumption level in the future relative to the consumption level now. Growth, social utility or welfare functions are used to evaluate consumption. The rate of the fall of the discount factor is called the discount rate which is used to estimate the present values of future costs and benefits of the adaptation options. The selection of an appropriate discount rate is another important factor in the determining the ‘desired’ adaptation options. This depends to large extent on the timing of the realisation of costs and benefits of the options.

Equity

The climate change impacts are disproportionately distributed and affect the vulnerable populations most of whom are poor. Therefore planners should consider the distribution of costs and benefits in addition to the net benefits. This aspect can be done by assigning weights to different costs and benefits depending on who bears the cost or receives the benefits. In practice, there are practical problems with deciding the weighting coefficients and location of thresholds. Most of the studies present the distributional impacts along with aggregate costs and benefits to allow policy makers to take the call.

Equity becomes important while making decisions regarding the choice of discount rate because of the difference between the timing of investments in adaptation and the realisation of benefits. The choice of discount rate according the Ramsey rule [3] would depend on the choice of two parameters; the rate of pure time preference and the coefficient of relative risk aversion. A low value of the former would imply intergenerational equity while a high value of the latter implies equity of space and time. In order to be able to better address concerns of equity it would make sense to have a range of discount rates while evaluating adaptation options rather than settling on one rate (World Bank 2009).

Questions of equity would also rise in the context of; (a) the aggregation of costs and benefits when there are groups within a generation with different levels of income and (b) the relevance or suitability of cost benefit assessments when these costs and benefits are unequally distributed within or across generations. How can these equity issues be addressed? Can application of an "equity factor" or assigning "equity weights" during the process of aggregation be a suitable method? The choice of the social welfare function [4] to aggregate the cost-benefit estimates is the ethical issue here. (World Bank 2010)

Assessment of Costs and Benefits of Adaptation

Three approaches to the assessment of costs and benefits of adaptation have been discussed below.

The Cost Benefit Analysis (CBA) is used to assess adaptation options when efficiency is the only decision making criteria. A CBA would involve the calculation and comparison of the costs and benefits in monetary terms. It provides a basis for prioritising possible adaptation measures and also gives an idea about the likely efficiency of an adaptation investment.

This has the benefit of comparing diverse impacts using a single metric. But it also important to be clear about the distribution of the costs and benefits and it would also be challenging to estimate reliably the values of things which are not valued in markets. For example the costs and benefits associated with environment goods and services, social or cultural values. Thus non market costs and benefits cannot be adequately captured in this analysis and thus the results can be misleading. Figure 2 shows the steps in assessing adaptation options using CBA.

The Cost Effectiveness Analysis (CEA) helps to ascertain the least costly adaptation option or options for meeting selected targets. The objectives of adaptation measures are usually already known and the task would be only to find the lowest cost options and not evaluate the justification of the measure. This is used in fields in which the adaptation benefits are difficult to express in monetary terms like human health, extreme weather events, biodiversity systems etc but the costs can be quantified. CEA is not often used as a standalone tool for decision making as the benefits are defined only by cost-effectiveness whereas other dimensions or equity, feasibility etc are not considered in the main analysis but may be looked at during the selection process of the chosen options. Figure 2.3 shows the steps involved in CEA.

Figure 2. Steps in Assessment of Adaptation Option using CBA

Source: Adapted from UNFCCC 2011 [5] 

Assessment of different adaptation options against a number of criteria can be done through Multi-Criteria analysis (MCA). Each criterion is given a weighting which is used to generate an overall score for the adaptation option and the one with the highest score is selected. MCA offers an alternative for assessment when only partial data is available, it is difficult to quantify cultural and ecological considerations and monetary benefit or effectiveness are just two of the many criteria. MCA was used by the least developed countries to prepare their national adaptation programmes of action. The advantage of MCA is that both quantitative and qualitative information can be incorporated. Moreover, MCA also caters for participatory engagements by allowing beneficiaries to be involved in choosing them which would important during the subsequent implementation. Assigning weight and standardizing scores are points of difficulty in MCS, particularly if the numbers of criteria are large and they are very different in character. Figure 2.4 shows the steps involved in MCA.

Figure 2. Steps in Assessment of Adaptation using CEA

Source: Adapted from UNFCCC 2011

Figure 2. Steps in Assessment of Adaptation using MCA

Source: Adapted from UNFCCC 2011

Infrastructure and Adaptation

The term infrastructure is generally defined as the "structural elements of an economy that facilitate the flow of goods and services between buyers and sellers" (Macmillan Dictionary of Modern Economics 2008 as cited in Parry et. al. 2009) and includes roads, bridges, railways, airports, ports, electric power systems, telecommunication systems, water, sewerage and drainage/waste-water management systems. This definition is often also broadened to include what is called social infrastructure which includes services which enable social and economic activities. For example public transport, health care, education etc. Infrastructure plays a significant role in all economic activities and economic growth of a nation. Adaptation of infrastructure is crucial in many climate sensitive sectors. It has a role in protecting people and assets from direct and indirect impacts of climate change. Due to it long lifetime, it is particularly vulnerable to climate change. In addition, the investments on infrastructure are typically high, long term and largely irreversible (Agrawala and Fankhauser 2008).The IPCC Fourth Report noted that "climate change can threaten lives, property, environmental quality and future prosperity by increasing the risk of storms, flooding, landslides, heat waves and drought and by overloading water, drainage and energy supply systems" (Parry, Canziani, et al. 2007). This would also put severe stress on the existing infrastructure as well as increase or change the demand for infrastructure.

Different Sectors in Infrastructure

Hallegate (2008) has suggested a list of sectors in which decisions should take into account climate change because of the fact that they involve long term planning, sunk costs and some irreversibility in choices and will be exposed to changes in climatic conditions. These have been depicted in Table 2.1. Most of these come under purview of the broad category called infrastructure or play a major role in their planning

Table 2. List of sectors which should take climate change into account

Sector

Time scale (years)

Exposure

(not quantified, number of * signifies levels)

Water infrastructure (e.g. dams, reservoirs)

30-200

***

Land-use Planning

>100

***

Coastline and flood defences (e.g. dikes, sea walls)

>50

***

Building and housing (e.g. Insulation)

30-150

**

Transportation Infrastructures (e.g. ports, bridges)

30-200

*

Urbanism (e.g. urban density,

>100

*

Energy Production (e.g. nuclear plan cooling system)

20-30

*

Source (Hallegate, 2008)

Approaches of Adaptation in Infrastructure

According to the UNFCCC (2007), there are two types of adaptation in the infrastructure sector. The first on would be to make changes in the operations of infrastructure directly affected by climate change like coastal or water resources infrastructure. The second type of adaptation would by provision of supporting infrastructure services which helps the climate affected sectors or resources to cope up. These would include provisions of public health services, research and many other applications require supporting infrastructure like hospitals, clinics, disease monitoring systems, buildings for extension services, laboratories etc. This is similar to the approaches for adaptation discussed by Agrawala and Fankhauser (2008).

Issues in Estimation of Costs of Adaptation for Infrastructure

Adaptation costs for infrastructure assets have been one of the largest components of total adaptation costs in past estimates—the largest in the United Nations Framework Convention on Climate Change (UNFCCC 2007).

The experiences in high income nations indicate that for most locations it would be easy and reasonable to adapt most forms of infrastructure to the impact of climate change up to the year 2030. But it would be increasingly harder to do so with longer term horizons, especially where mitigation is unsuccessful. There are also high risk locations like the island states, coastal areas and locations which would have higher costs of adaptation (Hughes, Chinowsky and Strzepek 2010).

In developing countries however, there is no basis for estimating the costs of infrastructure adaptation due to the following (Satterthwaite and Dodman 2009)

The large deficit in infrastructure provision in most developing countries basically point to the fact that in addition to the issue of infrastructure adaptation, the existing provisions for infrastructure itself not up to the standards which would make enough allowance for the direct and indirect effects of climate change.

There is a lack of a detailed explanation of the costs of ensuring climate-resilient infrastructure include both eliminating the infrastructure deficit and adapting the existing infrastructure. Conventional methods for estimating adaptation costs for infrastructure are generally based on the cost of modifying existing climate-sensitive infrastructure – but this cannot produce valid estimates if there are very large deficiencies in infrastructure provision..

While there are some cost estimates for infrastructure adaptation for specific projects (mostly in developed countries), these do not offer much insights which are relevant for larger policy decisions. In addition, since so much adaptation requires local investments rooted in very particular local contexts by local authorities, households and community organizations, estimates for the investments needed for adaptation are impossible without detailed local costing – yet no detailed local costing for adaptation were found in any developing country. There are very few studies which have provided aggregated information at national, regional or global levels. Most of these studies have adopted a top-down approach

An overview of global studies on estimation of costs and benefits of infrastructure adaptation before the EACC study

Research on adaptation costs began in the 1990s with the intention of estimating the economic costs of climate change. But the main objective was to refine the understanding of the impacts of climate change and not per se estimate the adaptation costs. Adaptive response costs were included in order to make the estimates more accurate (Fankhauser 2009). The emergence of studies of aggregate adaptation cost estimates was in response to the realisation that adaptation was unavoidable and the increasing international support for adaptation. Most of them only concern adaptation in developing countries and deal only with capital costs rather than lifetime costs.

The existing studies of cost can be broadly divided into two broad groups; those who adopt and aggregate level analysis and those who use a more disaggregate approach. A number of studies (World Bank 2006; Stern 2007; UNDP 2007) clearly used an aggregate approach. An example of a more disaggregated approach, which adopts a sector-by-sector approach at the impacts of climate change and then calculates the required investments to deal with those impacts, is the Oxfam (2007) study. The 2007 UNFCCC study also adopted a more disaggregated methodology, at least for some sectors. The disaggregated approach is better in that it provides better estimates at a sectoral level while the aggregated approach is more basic and has a lot of assumptions that is difficult to be substantiated. But while implementing the disaggregated approach, there is significant uncertainty regarding the future developments in the economy and also difficulty to obtain reliable data to allow an accurate assessment of adaptation options. In addition we can also classify the global studies into two main types;

Investment and Financial Flow (I&FF) analysis and other similar aggregated assessments;

Economic Integrated Assessment Models (IAMs)

The Investment and Financial Flow analysis focus on the possible costs of planned adaptation based on simple and highly aggregated approximations and do not consider the benefits of adaptation and thus do not look at an full economic framework. Examples of these studies are The Stern Review (2007), Oxfam report (2007), UNFCCC (2007) etc. Economic IAMs looks at aggregated estimates of costs and benefits of adaptation and provide estimates at the global level for a wide range of metrics. They have also been used in regional, national assessments. The main advantage of these models is that they use an economic framework which includes a consideration of costs, benefits and residual damages. Thus they can provide a vise range of outputs and influence the various adaptation options. In addition, aggregated economics costs and benefits for the future time and present values can be obtained which can then be used for a cost benefit analysis. Finally, they allow quick analysis of a large number of possible scenarios, allowing uncertainty assessment, (e.g. the Monte Carlo analysis in the PAGE model). In all of the above areas, the IAMs provide insights that could not be produced by any other approaches.

The first generation estimates started with a study by the World Bank (2006) which has estimated adaptation costs for infrastructure as the costs of climate proofing new investment flows (World Bank, 2010).In this study a fraction of the current infrastructure was estimated to be climate sensitive and a markup factor was then assumed to reflect the costs of climate proofing the future climate investment. About 40 percent of the Official Development Assistance (ODA), 10 percent of Foreign Direct Investment(FDI) and 2 to 10 percent of Gross Domestic Investment (GDI) are assumed to be climate sensitive. Out of this it is assumed that the climate proofing costs are 10-20 percent of the financial exposure. Among these assumptions only the Official Development Assistance figure had some empirical grounding. It was derived from earlier OECD work about climate risks in six developing countries (Bangladesh, Egypt, Fiji, Nepal, Tanzania and Uruguay). The studies which followed after, (Stern, 2007; UNDP, 2007 and Oxfam, 2007) adopted similar approach with some adjustments to the parameter value (Fankhauser, 2009). The UNDP study also estimated the costs of strengthening social protection programmes and scaling up aid in other key areas, whereas the Oxfam study (2007) added to these the scaled up estimates based on both the NAPAs and NGO programmes. Oxfam concluded that the cost of adaptation in developing countries was "likely to be at least $50 billion annually". (Please see Table 2.3 for a comparison of World Bank (2006), Stern Review (2007) and UNDP (2007).

Table 2. Comparison of estimates of World Bank (2006), Stern Review (2007) and UNDP (2007).

Study

Time frame

Factors considered

Investment flow

(US$ billion)

% climate sensitive

Extra Cost of climate proofing (%)

Adaptation Cost

(US$ billion/per year)

World Bank (2006)

Present

GDI

1500

2-10

10-20

3-30

FDI

160

10

10-20

2-3

ODA

100

40

10-20

4-8

Total

9-41

Stern Review

Present

GDI

1500

2-10

5 – 20

2-30

FDI

160

10

5- 20

1-3

ODA

100

20

5- 20

1-4

Total

4-37

UNDP (2007)

Present

GDI

2724

2-10

5-20

3-54

FDI

281

10

5-20

1-6

ODA

107

17-33

5-20

1-7

Additional Adaptation

42 [6] 

Total

86-109

Source: Adapted from Agrawala and Fankauser 2008 and World Bank 2010

Note: The World Bank and Stern investment data are for the year 2000; UNDP uses 2005

In spite of similar methodology being used, estimates in these studies have a large range from $4 billion to over $100 billion. Since these studies use the current investment flows as the basis, it can be inferred that the numbers represent short term adaptation needs. According to Fankhauser (2009), the wide range of estimates indicates a fundamental problem with the chosen estimate methodology. There is insufficient empirical information about the size of mark ups for climate-proofing and thus the range of credible values becomes extremely large. Because of large investment flows, even minute changes in the parameters can alter results up to an order that is considerable. The first generation studies have been dismissed as not substantive by Parry et. Al (2009)

The United Nations Frameworks Convention on Climate Change (UNFCCC) commissioned five sector studies to get a better idea of investment needs for adaptation both globally and in developing countries for the year 2030 (UNFCCC 2007). A detailed bottom up analysis of agriculture, forestry, fisheries, water supply, human health, coastal zones and infrastructure was done to come with estimates of adaptation costs at both global levels and developing countries’ level. Projected investments in physical assets for 2030 from the OECD ENV-Linkage model were used as the basis for estimating additional investment and financial flows needed in the infrastructure sector. The projected investment in physical assets for 2030 based on the OECD ENV-Linkage model corresponds to the projection of the IEA WEO reference scenario (UNFCC2007). But it should be noted that the UNFCCC uses the same methodology of World Bank (2006) study for infrastructure, using insurance data to determine the share of climate sensitive investment. The steps to calculate the costs of adaptation of new infrastructure in the UNFCCC study are given below.

Estimation of global investment in gross fixed capital formation in 2030 ( coms to around three times the global investment in 2000) - $ 22.27 trillion

Multiplying a proportion of this to get the investments vulnerable to the impacts climate change (based on losses from weather disasters)(0.7% for Munich Re data or 2.7% for ABI data) - $153 -650 billion a year

5-20% of this total is taken as increase in capital costs needed for adaptation - $8-31 billion (Munich Re data) or $33 – 130 billion (ABI data)

Table 2.3 gives the estimates of the UNFCCC study for various regions. Parry et al. (2009) have criticized the UNFCCC estimate in terms of its scope, depth and costing which leads them to an underestimation of the true costs of adaptation by at least a factor of two or three. Three weaknesses of this approach have been discussed by the authors;

The estimates for global investment in 2030 don’t include investment that are needed for infrastructure but is just an extrapolation of data for 2000. Thus the vast infrastructure deficiencies in the provision of infrastructure have not be taken into account. A continent like Africa which has 17.6% of the world’s population in 2030 is shown to have only 2.2% of the world investment!

Only data from large events are included in the Munich Re data which can lead to a vary huge underestimation of the costs of extreme weather disasters

Estimation of the costs of adaptation of infrastructure cannot be done by adding a percentage of existing investment if there no current existing investments.

Table 2. UNFCCC figures for additional investments needed to adapt infrastructure to climate change risks in 2030 (millions of US $)

Region

Munich Re data

ABI data

5 % additional investment

20% additional investment

5 % additional investment

20% additional investment

Africa

0.022

0.087

0.092

0.37

Developing Asia

1.9

7.6

8.1

32.42

Middle East

0.066

0.26

0.28

1.12

Latin America

0.40

1.62

.72

6.90

OECD countries

5.20

20.83

22.20

105.57

Transition economies

0.024

0.097

0.10

0.41

Total

7.62

30.50

32.51

130.06

Source: (UNFCCC 2007)

The figures therefore suggest that infrastructure in OECD countries will require 170 times more investment to adapt to climate change than will infrastructure in Africa. This is because there is much higher levels of infrastructure in these regions which would need to be protected. Satterthwaite and Dodman (2009) have come up with their own estimates by addressing issues of infrastructure deficit. This is shown in Table 2.4

Some other assumptions of UNFCCC are also questionable in terms of the estimates. It is stated that adaptation funding from international agencies is the "solution" for effective adaptation. But in many of the developing countries in Asia, Africa and parts of Latin America have "weak, ineffective and unaccountable" local governments. In addition, there existing technology and technical expertise in these countries may also not be sufficient to address the complexity of adaptation. This will adversely affect the capacity to design and implement appropriate adaptation strategies. Secondly, the assumption that adaptation and development can be kept separate is unreasonable. Impacts of climate change may worsen the non-climate change impacts. The existing institutional and governance structures may not be able to address nor be able to respond to either of these. Finally the assumption that the National Adaptation Programmes of Action (NAPA) gives an indication of the required adaptation costs is also not correct as the focus of most of these are a very small part of what the nations may actually need. Thus they do not form a good basis for assessing adaptation costs.

Table 2. Total estimated costs for removing the housing and infrastructure deficit by 2030 plus additional total costs for adapting to climate change

Region

Total cost (US$ billion)of:

Annual costs over 20 years (US$ billion) of

Removing Infrastructure deficit

Adapting to climate change (5%)

Adapting to climate change (20%)

Adapting to climate change (5%)

Adapting to climate change (20%)

Africa

1232

61.6

246.4

3.1

12.3

Low and middle income nations in Asia

4350

217.5

870

10.9

43.5

Latin America and the Caribbean

744

37.2

148.8

1.9

7.4

Total

6326

316.3

1256.2

15.9

63.2

Source: (Parry et al 2009)

), crude birth rate, infant mortality and some geographical features which would be country-fixed effects [7] .

Figure 0. Steps for calculatings costs of adaptation of infrastructure

Source: (World Bank 2010)

A consistent set of future population and GDP projections were used to develop baselines across sectors which reflects the A2 SRES scenario. The GDP trajectory is based on the average of the GDP growth projections of the three major integrated models of global emissions - ; (a) Climate Framework for Uncertainty, Negotiation, and Distribution FUND, (b) PAGE2002 and (c) Regional dynamic Integrated model of Climate and the Economy (RICE99) and - growth projections used by the International Energy Agency (IEA) and the Energy Information Administration of the United States Department of Energy to forecast energy demand. This is done in order to ensure consistency with the emissions projections. 2005 is treated as base year for all estimates and the World Development Indicators published in 2008 is the source for most data.

Climate Scenarios and Global Climate Models. The Community Climate System Model 3 (CCSM3) of the National Centre for Atmospheric Research (NCAR) and Mk 3.0 model of the Commonwealth Scientific and Industrial Research Organization (CSIRO) were used to for modelling climate change. The projections for these two models were created at a 0.5 by 0.5 spatial degree scale and a monthly time scale. This was done by applying the predictions through 2050 to the historical climate baseline obtained from the University of East Anglia Climate Research Unit’s Global Climate Database time series 2.1.

Methodology to estimate costs of adaptation for infrastructure

Costs for infrastructure adaptation have formed a major chunk of total adaptation costs as seen in previous estimates for total adaptation. In this study the infrastructure has been given a broad definition and includes transport (especially roads, rail, and ports),electricity, water and sanitation, communications, urban and social infrastructure, urban drainage, urban housing, health and educational facilities and general public buildings (Hughes, Chinowsky, & Strzepek 2010; World Bank 2010). For this sector the design standard updates and the maintenance and operations are the adaptation measure considered while calculating the costs.

For the purpose of estimation, the study defines an efficient level of provision of infrastructure which would be reached if the "country had invested up to the point at which the marginal benefits of additional infrastructure just cover the marginal costs—both capital and maintenance—of increasing the stock of infrastructure" (Hughes, Chinowsky and Strzepek 2010). It considers the development deficit is different from adaptation deficit.

The various equations involved in the calculation of the costs of adaptation are elaborated below.

The basic equation of total value of investment in infrastructure type i in a country j and period t is depicted as;

Iijt = Cijt(Qijt+1 – Qijt + Rijt) --------(1)

Where Cijt is the unit cost of investment and Rijt is the quantity of existing infrastructure of type i to be replaced during the period. When the change in total costs have to be calculated with respect to relevant climate variables it is done in terms of total differential of the above equation that either would affect the unit costs or efficient levels of provision of infrastructure. Thus it would be written as

∆Iijt = ∆C (Qijt+1 – Qijt + Rijt) + (C +∆C) ( Qijt+1 – Qijt + Rijt)--------- (2)

The stocks of various kinds of infrastructure are projected over 2010 under the development baseline without climate change. The additional costs of constructing, operating and maintaining these baseline levels of infrastructure services under the new climatic conditions as projected by the CCSM3 and Mk 3.0 global climate models are computed as adaptation costs. These are referred to as the delta-P [8] cost of adaptation and focuses on price and cost changes for fixed quantities of infrastructure. Thus the delta-P component calculates the costs of "climate-proofing" [9] the baseline projections of infrastructure assuming no climate change with estimates of the percentage changes in the unit costs of constructing, operating, and maintaining infrastructure as a consequence of climate change. The delta-Q costs are more difficult to estimate because they deal with change in infrastructure requirements due to impacts of climate change and according to the study there are lot uncertainty associated with this and also a need for extensive research. Thus in the final EACC report on delta-P costs of adaptation are included in the finals costs of adaptation. The econometric analysis was thus conducted with the purpose of determining the how a given climate affects the demand for infrastructure and not how change in climate would lead to change in the demand. The results suggest that demand for some types of infrastructure is impacted by different climate variables along with significant interactions with income per capita and urbanization.

was centred on the proposition that a major building code update would lead to a 0.8 per cent increase in construction costs of buildings and paved roads which would be required for every 10cm increase in annual precipitation. Code updates for paved roads related for first one degree rise in the maximum of monthly maximum would be .29 per cent followed by the a .29 per cent rise every three degree rise in maximum of monthly maximum temperatures.

The direct response [10] methodology was used for bridges and unpaved roads. In this method the changes in construction costs are directly associated with the specific alterations in climate or infrastructure design specifications. Thus for unpaved roads, the construction costs would increase by 0.8 per cent for every 1 per cent increase in monthly maximum temperature. For bridges, there is a 3.13 per cent costs for each one foot increase in clearance [11] .

and (b) the cost of preventing such a reduction in life span. [12] 

Table 0. Description of Dose Response for maintenance

Precipitation Dose Response

Temperature Dose Response

Methodology

Paved Roads - Existing

Change in annual maintenance costs per km per 10 cm change in annual rainfall projected during lifetime

Change in annual maintenance costs per km per 1 º

change Celsius in maximum of monthly maximum temperature

projected during lifespan.

Avoided Lifetime Decrement

Paved Roads –New

Paved roads constructed after 2010 would have no maintenance impact if designed for changes in climate

expected during their lifetime

Unpaved Roads

Increase by 0.8% with every 1 per cent increase in the maximum

of the maximum monthly precipitation values projected

for any given year.

Not estimated. Impact likely to be minimal

Direct Response

Railroads

Not estimated. Impact likely to be minimal

0.14% increase with every 1 decre increase in maximum of monthly maximum temperature

Direct Response

Buildings – Existing

Change in annual maintenance costs per

square foot per 10 cm change in annual

rainfall

Change in annual maintenance costs per square foot per

1 º change Celsius in annual average temperature

Avoided Lifespan Decrement

Buildings - New

Buildings constructed after 2010 would have no maintenance impact if designed for changes in climate expected during their lifetime.

Source: (Chinowsky, Price and Neuman, 2010)

Direct response methodology was used to calculate the dose-response values for the costs of maintenance for railroads and unpaved roads which is similar to the method discussed earlier for the construction costs of unpaved roads and bridges.

Results and Analysis

According to the final report of the EACC study (World Bank 2010) on global estimates the average cost estimates for infrastructure adaptation for developing countries as a whole are between $14 to $ 30 billion per year. The EACC report has not considered a deep analysis of CSIRO scenario because of the low costs that are predicted by it and thus this section would also focus on the NCAR scenario for future analysis.

In the report on infrastructure (Hughes, Chinowsky and Strzepek 2010), the costs of adaptation for infrastructure from 2010-50 was presented classified by economic categories and infrastructure sectors for the whole world which has been shown in Table 3.3.It is seen that the total adaptation costs per year for infrastructure in the world till 2050 comes to only $ 43 billion USD which is just 1 per cent of the total baseline investments projected for the same period. The adaptation costs as a percentage of baseline costs are the lowest for high income countries at 0.79 per cent while for the low income countries it is 1.69 per cent.

As can be seen in figure 3.2 adaptation costs for urban infrastructure forms the major part of total costs at 47 per cent followed by roads at 28 per cent. But if we examine the adaptation costs as percentage of baseline costs it can be seen that the highest costs are for other transport at 4.2 per cent followed by roads at 3 per cent. The results also state that the adaptation costs are going to be the highest for the high income countries at $ 15 billion USD.

Figure 3.3 shows the how adaptation costs are distributed among low income, lower middle income, higher middle income and high income countries. It was seen that the high income countries would have to bear the highest adaptation costs approximately 35 per cent of the total adaptation costs. Again this points to the fact that their baseline investments itself are so high that adaptation costs as a percentage of that would be higher tha

1

Table 4.3 shows the indicative estimates of maintenance cost over the lifespan of the roads. This increase in costs would be required if the building codes and standards are not updated. In the EACC study it is assumed that all infrastructures constructed after 2010 would be constructed with updated codes and thus there would be no increase in maintenance costs for them.

The freeze-thaw analyses basically consisted of comparing the timing of critical FI, critical TI and the length of the freeze season (sum of days between FI and TI) for baseline and climate change scenarios. The baseline median duration to reach critical FI ranged from 58 days to 116 days and the same to reach critical TI ranged from 131 to 187 days. The days to reach critical FI increased and the days to reach critical TI decreased under the climate change scenarios. Under the CGCM2A2x scenarios, 50 per cent of all seasons fail to reach critical FI (and thus critical TI also) in 5 (Kelowna, Windsor, Vancouver, Toronto and St. Johns) sites and among the rest of the sites the median duration of days required to reach critical FI increase in range of 4 (North Bay) to 27 days (Halifax). The days to reach critical TI range from 10 (Thunder Bay) to 31 days (Muskoka). Under the HadCM3B21 scenario, critical FI is achieved in at least 50 per cent of all season at all sites except Vancouver. The median values for most sites increases by one to two weeks except for five sites (Kelowna, Toronto, Windsor, Halifax and St. John’s) where the median is increased by 28 days. In all sites except Kelowna, Critical TI is achieved earlier in the season with the median duration ranging from 2 to 14.5 days earlier than baseline. As Kelowna, median values increase by 3 days. The baseline values for the mean duration of the freeze season ranged from 0 days in Vancouver to 122 days in Winnipeg while the baseline standard deviation for most sites ranged from 15-20 days for most sites except Vancouver which had no freeze season and Calgary and Edmonton where there was greater variability. There was a substantial drop in the mean duration of the freeze season from 8 per cent at Winnipeg (HadCM3B21) to 98 per cent at St John’s and Windsor sites (CGCM2A2x). The CGCM2A2x scenario produced more reductions in the season lengths.

1240

SM

2.7

19.1

Next, this study discusses the various impacts of climate change on roads which would result from high temperatures, extreme precipitation/floods, sea level rise and permafrost degradation. This is shown in Table 5.4.

For the freight transport task, a similar approach was used as illustrated in Equations 2 and 3.

Equation 3

(Austroads 2004