Tuesday, July 5, 2011

The IPCC v the 21st century

Some people complain that the IPCC is intransparent. David Holland is a good example. Such sentiments are often expressed with a suggestion that this is because the IPCC is arrogant or malign. There is a third explanation: The IPCC has not quite woken up to this internet thingy.

One example is the IPCC policy to keep draft assessment reports under wraps and release them with a big bang. That was never possible. I recall excited journalists waving faxed copies of draft pages of AR2. Technology has moved on. The 0th order draft of AR5 will soon be available in its entirety on the internet. The problem with the IPCC policy is that stimulates uncontrolled leaks. The first rule of PR is to keep control over the message.

I think that the IPCC has stuck to its policy because the Bureau is full of people who are not particularly internet savvy.

The same seems to be true of IPCC authors. Search engines, reference management software, and document management software are science fiction to some of my colleagues. That affects transparency. If properly managed, you can release the entire history of a document with a few clicks of a mouse. If your documents are not properly managed, you'd be hard-pressed to do that. It also affects quality. Without a search engine, you quote the papers you know -- and the papers you know are by the people you know. With a search engine, you cite all papers -- not necessarily unbiased, but at least comprehensive.

Sunday, July 3, 2011

Zeroth Order Draft

We just submitted the Zeroth Order Draft, for friendly review by self-selected peers -- 32 hours before the deadline, 900 words below target, and reasonable contents too.

It is summer 2011. AR5 will be published in summer 2014. IPCC reports survey a fast-moving literature. Either AR5 will be outdated before it appears, or the early reviews are largely pointless.

That said, we now have a rough idea of the contents of the other chapters, so that coordination can being in earnest. Chapter 10 (key economic sectors and services) has substantial links to Chapters 3 (water), 4 (water), 5 (coasts), 6 (oceans), 7 (food), 11 (health), 14 (adaptation), 15 (adaptation), 16 (adaptation), 17 (adaptation) and 19 (vulnerability); it may overlap with Chapters 8 (cities), 9 (countryside), 12 (security) and 13 (poverty); and Chapter 18 (detection) may expect us to do things that we expect them to do.

Zeroth Order Draft Recreation and Tourism

10.6.       Recreation and tourism

Recreation and tourism is one of the largest sectors of the economy. It accounts for a substantial share of consumer spending in rich countries, and employs many people. Supply of tourism services is the dominant activity in many regional economies.

Recreation and tourism encompass many activities, some of which are more sensitive to weather and climate than others: compare sunbathing to angling, gambling, business seminars, family visits, and pilgrimage. Climate change would affect the place, time and nature of these activities.

There is a large literature on the impact of climate change on tourism.  Some studies focus on the changes in the behavior of tourists, that is, the demand for recreation and tourism services (see 10.6.1). Other studies look at the implications for tourists resort, that is, the supply of recreation and tourism services (see 10.6.2). A few studies consider the interactions between changes in supply and demand (see 10.6.3).

10.6.1.    Recreation and tourism demand

Conventionally, recreation does not involve an overnight stay whereas tourism does. That implies that recreation, unlike tourism, is done close to home. Whereas tourists, to a degree, chose the climate of their holidays, recreationists do not (although climate is a consideration in the choice where to live). Tourists would adapt to climate change by changing the location, timing and activities of their holidays; recreations would adapt only timing and activities (Smith, 1990).        Recreation

There has been no research on systematic differences of recreational behaviour due to differences in climate. The impact of climate change on recreation is therefore unknown. The economic impact is probably limited, as people are more likely to change the composition rather than the level of their time and money spent on recreation. For instance, (Shaw and Loomis, 2008) find a likely increase, due to climate change, in boating, golfing and beach recreation at the expense of skiing.

There are case studies of the impact of climate change on recreation.(Dempson et al., 2001) note that the salmon fishery in Newfoundland is closed during hot weather and low water levels. (Ahn et al., 2000) study the impact of climate change on recreational trout fishing in the Southern Appalachian Mountains. (Whitehead et al., 2009) study the effect of sea level rise on sea shore fishing in North Carolina. Both studies find a substantial decrease in the value recreationists would derive from these activities – so much so that one could expect people to adopt other ways of enjoying themselves. Such alternatives were unfortunately excluded from the studies. Similarly, (Daugherty et al., 2011) conclude that climate change will make it more difficult to guarantee adequate water levels for boating and angling in artificial reservoirs – but do not study what recreationists would do instead. (Pouta et al., 2009) project a reduction in cross-country skiing in Finland, particularly among women, the lower classes, and urban dwellers. (Shih et al., 2009) find that weather affects the demand for ski lift trips. There are positive effects too. (Richardson and Loomis, 2005) find that climate change would make trips to the Rocky Mountain National Park more enjoyable. (Scott and Jones, 2006; Scott and Jones, 2007) foresee an increase in golf in Canada due to climate change, (Kulshreshtha, 2011) sees positive impacts on Canadian recreation in general, and (Coombes et al., 2009) predict an increase in beach tourism in East Anglia; but none of these studies accounts for budget constraints on time or money.

Some studies confuse weather and climate, or suffer from selection bias. For instance, (Graff Zivin and Neidell, 2010) find that people recreate indoors when the weather is inclement. (Scott et al., 2007) estimate the relationship between visitors to Waterton Lakes National Park and weather variables for eight years of monthly observations; and use this to project an increase in visitor numbers due to climate change. A survey among current visitors indicates that a deterioration of the quality of nature would reduce visitor numbers. (Taylor and Ortiz, 2009) estimate the impact of weather on domestic tourism in the UK, finding that tourists often respond to past weather. The hot summer of 2003 had a positive impact on revenues of the tourist sector. As another example, (Denstadli et al., ) find that tourists in the Arctic do not object to the weather in the Arctic. (Gössling et al., 2006) reaches the same conclusion for tourists on Zanzibar. Neither study assesses the representativeness of their sample of all tourists.        Tourism

Climate (Braun et al., 1999; Gómez Martín, 2005; Wall and Badke, 1994) and weather (Agnew and Palutikof, 2006; Garbas, 2006; Rossello, 2011; Rosselló-Nadal et al., 2010; Álvarez-Díaz and Rosselló-Nadal, 2010) are important factors in tourist destination choice. (Maddison, 2001) estimates a statistical model of the holiday destinations of British tourists.  (Lise and Tol, 2002) replicate this for Dutch tourists and (Bigano et al., 2006) for tourists from 45 countries. Tourists have a clear preference for the climate that is currently found in Southern France, Northern Italy and Northern Spain. People from hot climates are more particular about where they spend their holidays than people from cool climates.

However, whereas (Bigano et al., 2006) find regularity in revealed preferences, (Scott et al., 2008b) find pronounced differences in stated preferences. This suggests that the impact of climate change on tourism demand may be more complicated than suggest by the econometric analyses reviewed above (Gössling and Hall, 2006).

(Bigano et al., 2007; Hamilton et al., 2005a; Hamilton et al., 2005b) use the above econometric analyses to construct a simulation of domestic and international tourism. (Hamilton and Tol, 2007) downscale the national results of these studies to the regions of selected countries. The advantage of such a model is that it assesses the logical consequences of the econometric results, which is not trivial as all potential holiday destinations see a simultaneous change in their attractiveness.  The disadvantage is stylized representation of the effect of climate on destination choice. Two main findings emerge. First, climate change would drive tourists to higher latitudes and altitudes. International tourist arrivals would fall, relative to the scenario without warming, in hotter countries, and rise in colder countries. Tourists from Northwestern Europe, the main origin of international travelers at present, would be more inclined to spend the holiday in their home country, so that the total number of international tourists falls. Second, the impact of climate change is dominated by the impact of population growth and, particularly, economic growth. In the worst affected countries, climate change slows down the rate of growth in the tourism sector, but tourism nowhere shrinks.

10.6.2.    Recreation and tourism supply

There are a number of so-called biometeorological studies of the impact of climate change on tourism. (Yu et al., 2009a) construct a Modified Climate Index for Tourism and apply it to fifty years of past data for Alaska and Florida. They find that Alaska has become more attractive, and Florida less attractive to tourists. (Yu et al., 2009b) use the same approach to conclude that the climate for sightseeing has improved in Alaska, while the climate for skiing has deteriorated. (Scott et al., 2004) use a similar index. Climate change would make Mexico less attractive to tourists, and Canada more attractive. Florida and Arizona would lose market share in US tourism. (Perry, 2006) notes that the hot summer of 2003 had a negative impact on tourism in the Mediterranean. (Matzarakis et al., 2010) construct a composite index of temperature, humidity, wind speed and cloud cover, and use this to map tourism potential. (Lin and Matzarakis, 2011) apply the index to Taiwan and Eastern China. (Endler and Matzarakis, 2010a; Endler and Matzarakis, 2010b; Endler and Matzarakis, 2011)use this index to study the Black Forest in Germany in detail, highlighting the differences between summer and winter tourism, and between high and low altitudes; the latter aspect is thoroughly investigated by (Endler et al., 2010). (Matzarakis and Endler, 2010) uses this method to study Freiburg. (Matzarakis et al., 2007) use the same method to project this potential into the future, finding that the Mediterranean is likely to become less attractive to tourists. (Amelung and Viner, 2006; Giannakopoulos et al., 2011; Hein et al., 2009; Perch-Nielsen et al., 2009) use a different index to reach the same conclusion, but also point out that Mediterranean tourism may shift from summer to the other seasons. (Giannakopoulos et al., 2011) notes that coastal areas in Greece may be affected more than inland areas because, although temperature would be lower, humidity would be higher. (Moreno and Amelung, 2009), on the other hand, conclude that climate change will not have a major impact on beach tourism in the Mediterranean (at least not before 2050) because sunbathers like it hot. (Amelung et al., 2007) use a weather index for a global study of the impact of climate change on tourism, finding shifts from equator to pole, summer to spring and autumn, and low to high altitudes. (Perch-Nielsen, 2010) combines a meteorological indicator of exposure with indicators of sensitivity and adaptive capacity. She uses this to rank the vulnerability of beach tourism in 51 countries. India stands out as the most vulnerable, and Cyprus as the least vulnerable.

The main criticism of most biometeorological studies is that the predicted gradients and changes in tourism attractiveness have rarely been tested to observations of tourist behaviour. (De Freitas et al., 2008) validate their proposed meteorological index to survey data. (Moreno et al., 2008) and (Ibarra, 2011) use video of beach occupancy to test meteorological indices for beach tourism. (Gómez-Martín, 2006) tests meteorological indices against visitor numbers and occupancy rates.

Other studies put tourists centre stage. (Eijgelaar et al., 2010) argues that so-called “last chance tourism” is a strong pull for tourists to visit Antarctica to admire the glaciers while they still can. (Farbotko, 2010) uses a similar mechanism to explain the rise in popularity of Tuvalu as a destination choice.

Studies on the supply side often focus on ski tourism.  (Abegg and Elsasser, 1996) is one of the earliest papers. Under their particular climate scenario, a warming of 2ºC would raise the altitude of snow-reliable resorts by 300 metres in the Swiss Alps; 22% fewer resorts would be snow-reliable. (Elsasser and Bürki, 2002) point out that artificial snow-making cannot fully offset the loss in natural snowfall. (Hamilton et al., 2007) reaches a similar conclusion for New England, highlighting the importance of “backyard snow” to induce potential skiers to visit ski slopes. (Pickering et al., 2010) find a preference of skiers in Australia of natural snow over artificial snow. From a series of interviews, (Hill et al., 2010) find that tourist operators in the Swiss Alps seek to maintain the status quo through adaptation, rather than search for viable alternatives to ski tourism; and argue that better coordination is needed for adaptation to be successful. (Scott and McBoyle, 2007) highlight that there are many options to adapt to a loss of snow for skiing. (Hoffmann et al., 2009) use a survey of ski lift operators in the Swiss Alps and find that adaptation measures are driven by the ability to adapt (rather than the need) and that adaptation is more prevalent on higher slopes (which are less vulnerable). (Scott et al., 2006) study the impact of climate change on six ski areas in eastern North America. Even with snowmaking, climate change could be an existential threat to 3 of the 6 ski areas by 2050; and climate change would lead to a contraction in each area in each scenario. (Dawson et al., 2009) use past analogues to study the impact of future climate change on ski tourism in the Northeastern USA. They find that small and very large resorts will be hit hardest. (Scott et al., 2008a) find that snowmobiling would have disappeared from the Northeastern USA by the end of the 21st century. Artificial snowmaking would halt the decline of ski resorts, but water scarcity and the costs of snowmaking would be increasingly large problems. (Scott et al., 2003) reach the same conclusion for southern Ontario, (Scott et al., 2007) for Quebec, and (Steiger and Mayer, 2008) for Tyrol. (Bicknell and Mcmanus, 2006) study adaptation for ski resorts in Southeastern Australia. They note that resorts may continue to be economically viable in the absence of snow by focusing on alternative activities. (Pickering and Buckley, 2010) note that artificial snow-making may be infeasible and uneconomic at the scale required to offset the loss of natural snow in Australia, and argue for a reorientation towards summer tourism and residential property development. (Moen and Fredman, 2007) find that alpine ski resorts in Sweden would become economically unviable, and that alternative livelihoods need to be developed. (Tervo, 2008) finds that the shortening of the Finnish ski season would be too limited to affect the economic viability of tourist operators. (Serquet and Rebetez, 2011) find that the Swiss Alps attract more tourists during hot summers, and argue that climate change would structurally improve the mountains as a summer tourism destination. (Bourdeau, 2009) argue along the same lines for the French Alps, stressing the importance of non-tourism alternatives as a source of economic development. (Potocka and Zajadacz, 2009) argue that prudent management supplies tourism services suitable for all weather.

Other studies consider beach tourism. (Phillips and Jones, 2006) focuses on beach erosion due to sea level rise, and the various options to prevent that. (Hamilton, 2007) finds an aversion against artificial coastlines, so that hard protection measures against sea level rise would reduce the attractiveness of an area for recreation and tourism. (Raymond and Brown, 2011) survey tourists on the Southern Fleurieu Peninsula. They conclude that tourists who are there for relaxation worry about climate change, particularly sea level rise, while tourists who are there to enjoy nature do not share that concern.  (Becken, 2005) finds that tourist operators have adapted to weather events, and argues that this helps them to adapt to climate change. (Belle and Bramwell, 2005) find that tourist operators on Barbados are averse to public adaptation policies. (Uyarra et al., 2005) find that tourists on Barbados would consider holidaying elsewhere if there is severe beach erosion. (Buzinde et al., 2010a; Buzinde et al., 2010b) find that there is a discrepancy between the marketing of destinations as pristine and the observations of tourists, at least for Mexican beach resorts subject to erosion. They conclude that, contrary to official preconceptions, tourists are not deterred by environmental change.

Some studies focus on nature tourism. (Wall, 1998) notes the impact of climate change on water-based tourism, on the coast through sea level rise and inland through drought. (Cavan et al., 2006) find that climate change may have a negative effect on the visitor economy of the Scottish uplands as natural beauty deteriorates through increased wild fires. (Saarinen and Tervo, 2006) interviewed nature-based tourism operators in Finland, and found that about half of them do not believe that climate change is real, and that few have considered adaptation options. (Nyaupane and Chhetri, 2009) argue that climate change would increase weather hazards in the Himalayas and that this would endanger tourists. (Uyarra et al., 2005) find that tourists on Bonaire would not return if coral was bleached. (Hall, 2006) finds that small tourist operators in New Zealand do not give high priority to climate change, unless they were personally affected by extreme weather in recent times. The interviewed operators generally think that adaptation is a sufficient response to climate change for the tourism sector. (Wang et al., 2010) note that glacier tourism is particularly vulnerable to climate change, highlighting the Baishiu Glacier in China.

A few studies consider all aspects of the impact of climate change for particular countries or regions. (Ren Guoyu, 1996) shows that domestic tourism in China will shift northwards, that sea level rise would damage some tourist facilities, and that the overall impact of climate change on China’s tourist sector would be negative. (Harrison et al., 1999) conclude that climate change would make Scotland less attractive to tourists in winter but more attractive in summer. (Ceron and Dubois, 2005) assess the impact of climate change on tourism in France. They argue that the French Riviera may benefit because it is slightly cooler than the competing coastal resorts in Italy and Spain. The Atlantic Coast, although warming, would become less attractive because of increased rainfall. The increase in summer tourism in the mountains is unlikely to offset the decrease in winter tourism. (Jones et al., 2006) study the impact of climate change on three festivals in Ottawa. They argue for heat wave preparedness for  Canada Day, find that skating on natural ice may become impossible for Winterlude, and fret that the dates of the Tulip Festival may need to be shifted to reflect changing phenology.  (Dawson and Scott, 2010) assess the impacts in the Great Lakes regions, finding reduced tourism potential in winter but increased opportunities in summer. (Turton et al., 2010) study Australia. They conclude that tourist operators find the uncertainty about climate change too large for early investment in adaptation.

10.6.3.    Market impacts

There are only two papers that consider the economic impacts of climate-change-induced changes in tourism supply and demand. Both studies use a computable general equilibrium model, assessing the effects on the tourism sector as well as all other markets. (Berrittella et al., 2006) consider the consumption pattern of tourists and their destination choice. They find that the economic impact is qualitatively the same as the impact on tourist flows (discussed above): Colder countries benefit from an expanded tourism sector, and warmer countries lose. They also find a drop in global welfare, because of the redistribution of tourism supply from warmer (and poorer) to colder (and richer) countries. (Bigano et al., 2008) extend the analysis with the implications of sea level rise. The impact on tourism is limited because coastal facilities used by tourists are sufficiently valuable to be protected against sea level rise. The study finds that the economic impacts on the tourism sector are reinforced by the economic impacts on the coastal zone; and that the welfare losses due to the impact of climate change on tourism are larger than the welfare losses due to sea level rise.
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Zeroth Order Draft Markets and Development

10.9.       Impacts on markets and development

Prior sections of this chapter present the direct impacts of climate change on the economy sector by sector. There are, however, also indirect impacts. The effects that impacts in one sector may have on the rest of the economy are initially presented, followed by the impacts on economic growth and development.

10.9.1.    General equilibrium effects

General equilibrium analysis describes how climate change impacts in one sector propagate to the rest of the economy; and how the changed macroeconomic context feedbacks on the sector. There are three channels through which impact diffuse. First, outputs of one sector are used as inputs to other sectors. For example, a change in crop yields would affect the food-processing industry. Second, products compete for the consumers’ finite budget. If, for example, food becomes more expensive, less money would be spent on other goods and services. Third, sectors compete for the primary factors of production (labor, capital, land, water). If more labor is needed in agriculture to offset a drop in crop yields, less labor is available to produce other goods and services. Firms and households react to changes in relative prices, domestically and internationally.

General equilibrium models provide a comprehensive and internally consistent analysis of the medium-term impact of climate change on economic activity and welfare. However, these models necessarily make a number of simplifying assumptions, particularly with regard to the rationality of consumers and producers and the absence of market imperfections.

Computable general equilibrium models have long been used to study the wider economic implications of changes in crop yields (Kane et al., 1992). (Yates and Strzepek, 1998) show for instance that the impact of a reduced flow of the Nile on the economy of Egypt is much more severe without international trade than with, because trade would allow Egypt to focus on water-extensive production for export and import its food.

Older studies focused on the impact of climate change on patterns of specialization and trade, food prices, food security and welfare (Darwin and Kennedy, 2000; Darwin, 2004; Kane et al., 1992; Reilly et al., 1994; Winters et al., 1998; Yates and Strzepek, 1998). This has been extended to land use (Lee, 2009; Ronneberger et al., 2009), water use (Calzadilla et al., 2011; Kane et al., 1992), and multiple stresses (Reilly et al., 2007). General equilibrium models have also been used to estimate the value of improved weather forecasts (Arndt and Bacou, 2000), a form of adaptation to climate change. Computable general equilibrium analysis has also been used to study selected impacts other than agriculture, notably sea level rise (Bosello et al., 2007; Darwin and Tol, 2001), tourism (Berrittella et al., 2006; Bigano et al., 2008), human health (Bosello et al., 2006) and energy (see 10.2).

(Bigano et al., 2008) study the joint impacts on tourism and coasts, finding that tourism dominates the welfare impacts. (Kemfert, 2002) and (Eboli et al., 2010) estimate the joint effect on the world economy of a range of climate change impacts, but conflate general equilibrium and growth effects. (Aaheim et al., 2010) analyze the economic effects of impacts of climate change on agriculture, forestry, fishery, energy demand, hydropower production, and tourism on the Iberian peninsula. They find positive impacts on output in some sectors (agriculture, electricity) negative impacts in other sectors (forestry, transport) and negligible ones in others (manufacturing, services). (Ciscar et al., 2011) study the combined effect of agriculture, sea level rise, river floods and tourism on the European economy. They find a welfare loss of 0.2-1.0% of income by the end of the century for the European Union. There are large regional differences with losses in Southern Europe and gains in Northern Europe.

The following initial conclusions emerge. First, markets matter. Impacts are transmitted across locations—with local, regional and global impacts-- and across multiple sectors of the economy.. For instance, landlocked countries are affected by sea level rise because their agricultural land increases in value as other countries face erosion and floods. Second, consumers and producers are often affected differently. The price increases induced by a reduction in production may leave producers better off while hurting consumers. Third, the distribution of the direct impacts can be very different than the distribution of the indirect effects.  For instance, a loss of production may be advantageous to an individual company or country if the competition loses more. Fourth, a loss of productivity or productive assets in one sector leads to further losses in the rest of the economy. At the same time, fifth, markets offer options for adaptation, particularly possibilities for substitution. This changes the size, and sometimes the sign of the impact estimate.

10.9.2.    Growth effects        The rate of economic growth

Climate change would also affect economic growth and development, but our understanding is limited.  (Fankhauser and Tol, 2005) investigate four standard models of economic growth and three transmission mechanisms: economic production, capital depreciation, and the labor force. They find that, in three models, the fall in economic output is slightly larger than the direct impact on markets – that is, the total impact is more than twice as large as the direct impact – while the 4th model (which emphasizes human capital accumulation) points to indirect impacts that are 1.5 times as large as the direct impacts. The difference can be understood as follows. In the three models, impacts crowd out consumption and investment in physical capital, while in the fourth model investment in human capital too is crowded out. (Hallegatte, 2005) reaches a similar conclusion. (Hallegatte and Thery, 2007; Hallegatte and Ghil, 2008; Hallegatte and Dumas, 2009) highlight that the impact of climate change through natural hazards on economic growth can be amplified by market imperfections and the business cycle. (Eboli et al., 2010) use a multi-sector, multi-region growth model. The impact of climate change would lead to a 0.3% reduction of GDP in 2050. Regional impacts are more pronounced, ranging from -1.0% in developing countries to +0.4% in Australia and Canada. Sectoral results are varied too, with output changes ranging from output of +0.5% for power generation (to meet increased demand to air conditioning) to -0.7% for natural gas (as demand for space heating falls) and rice.

Using a biophysical model of the human body’s ability to do work, (Kjellstrom et al., 2009) find that by the end of the century climate change may reduce labor productivity by 11-27% in the humid (sub)tropics. Assuming a output elasticity of labor of 0.8, this would reduce economic output in the affected sectors (involving heavy manual labor without air conditioning) by 8-22%. Although structural change in the economy may well reduce the dependence on manual labor and air conditioning would be an effective adaptation, even the ameliorated impact would have a substantial, but as yet unquantified, impact on economic growth.

In a statistical analysis, (Dell et al., 2009) find that one degree of warming would reduce income by 1.2% in the short run, and by 0.5% in the long run. The difference is due to adaptation. (Horowitz, 2009) finds a much larger effect: a 3.8% drop in income in the long run for one degree of warming. In a yet-unpublished study, (Dell et al., 2008) find that climate (change) has no effect on economic growth in countries with an income above the global median ($PPP,20003170) but a large impact on countries below the median. If companies can fully adapt to a new climate in 10 years time, economic growth in the 21st century would be 0.6% slower if climate changes according to the A2 scenario than in the case without climate change. If economic growth is 2.6% per year without climate change, and 2.0% with, then a century of climate change would reduce income by 44%.        Poverty traps

Poverty is concentrated in the tropics and subtropics. This has led some analysts to the conclusion that a tropical climate is one of the causes of poverty. (Gallup et al., 1999) emphasize the link between climate, disease, and poverty while (Masters and McMillan, 2001) focus on climate, agricultural pests, and poverty. Other studies (Acemoglu et al., 2001; Acemoglu et al., 2002; Easterly and Levine, 2003) argue that climatic influence on development disappears if differences in human institutions (the rule of law, education, etc) are accounted for. However, (Van der Vliert, 2008) demonstrates that climate affects human culture and thus institutions, but this venue has yet to be explored in the economic growth literature. (Brown et al., 2011) find that weather affects economic growth in Sub-Saharan Africa – particularly, drought decelerates growth. (Jones and Olken, 2010) find that exports from poor countries fall during hot years. (Bloom et al., 2003) find limited support for an impact of climate (rather than weather) on past growth in a single-equilibrium model, but strong support in a multiple-equilibrium model: Hot and wet conditions and large variability in rainfall reduce long-term growth in poor countries (but not in hot ones) and increase the probability of being poor.

(Galor and Weil, 1996) speculate about the existence of a climate-health-poverty trap. (Bonds et al., 2010) and (Strulik, 2008) posit theoretical models and offer limited empirical support, while (Tang et al., 2009) offers more rigorous empirical evidence. This is further supported by yet-to-be-published analyses (Bretscher and Valente, 2010; Gollin and Zimmermann, 2008; Gollin and Zimmermann, 2010; Ikefuji et al., 2010). Climate-related diseases such as malaria and diarrhea impair children’s cognitive and physical development. This leads to poverty in their later life so that there are limited means to protect their own children against these diseases. Furthermore, high infant mortality may induce parents to have many children so that their investment in education is spread thin. An increase in infant and child mortality and morbidity due to climate change would thus trap more people in poverty.

(Zimmerman and Carter, 2003) build a model in which the risk of natural disasters causes a poverty trap: At higher risk levels, households prefer assets with a safe but low return. (Carter et al., 2007) find empirical support for this model at the household level, but (van den Berg, 2010) concludes the natural disaster itself has no discernible impact on investment choices. At the macro-economic level, natural disasters disproportionally affect the growth rate of poor countries (Noy, 2009).

(Bougheas et al., 1999; Bougheas et al., 2000) show that more expensive infrastructure, for example because of frequent repairs after natural disasters, slows down economic growth and that there is a threshold infrastructure cost above which trade and specialization do not occur, suggesting another mechanism through which climate could cause a poverty trap. The implications of climate change have yet to be assessed.        Conclusion

In sum, the literature on the impact of climate and climate change on economic growth and development has yet to reach firm conclusions. There is agreement that climate change would moderate the rate of economic growth, by a little according to some studies and by a lot according to other studies. There is disagreement whether climate change would affect the nature of economic development, with some studies suggesting that more people may be trapped in poverty and fewer people enjoying exponential growth.

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