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Volume 1, Number 1, 2014, 9–24 journal homepage: region.ersa.org
Climate change in Lebanon: Higher-order regional im-
pacts from agriculture∗
Eduardo A. Haddad1, Nadim Farajalla2, Marina Camargo3, Ricardo L. Lopes4,
Flavio V. Vieira5
1Department of Economics and NEREUS, University of S˜ao Paulo, S˜ao Paulo, Brazil (email: ehad-
dad@usp.br)
2American University of Beirut, Beirut, Lebanon (email: nf06@aub.edu.lb)
3NEREUS, University of S˜ao Paulo, S˜ao Paulo, Brazil (email: mabc@usp.br)
4Maring´a State University, Maring´a, Brazil (email: rllopes@uem.br)
5Federal University of Uberlˆandia, Uberlˆandia, Brazil (email: flaviovieira@ufu.br)
Received: 15 June 2014/Accepted: 31 July 2014
Abstract. In this paper, we analyze the susceptibility of agricultural outputs to future
climate change in Lebanon, and the extent to which it propagates to the economic system
as a whole. We use a methodological framework in which physical and economic mod-
els are integrated for assessing the higher-order economic impacts of projected climate
changes. By using this integrated modeling approach, we are able to quantify the broader
economic impacts in the country by considering not only the temporal dimension but
also the regional disaggregation of the results. Our estimates suggest that there are high
potential costs and risks associated with a burden to the poorer and more vulnerable
regions of the country.
1 Introduction
Lebanon’s Second National Communication (SNC) to the United Nations Framework
Convention on Climate Change (UNFCC) (MoE 2011) made important advances in many
areas. A major improvement over the Initial National Communication to the UNFCC
(INC) (MoE 1999) refers to the climate modeling effort as the first time a specifically
developed regional model that targeted Lebanon was used. This allowed for the develop-
ment of climate change impact scenarios in various sectors. Data availability and a lack of
scientific studies, however, precluded further advances in strategic topics. One such topic
relates to the assessment of the impacts of climate change on the agriculture sector. The
report relied mostly on the qualitative analysis of indicators of climate change impacts
on vulnerable systems in agriculture. While the discussion did not include any effective
effort to modeling the relationships between projected changes in climatic conditions and
crop yields in Lebanese territory, it provided a targeted impact assessment that could
potentially be measured in the future.
∗This article was developed under the memorandum of partnership agreement between the Issam
Fares Institute for Public Policy and International Affairs at the American University of Beirut, and
the University of S˜ao Paulo Regional and Urban Economics Laboratory – NEREUS. The authors ac-
knowledge financial support by the Brazilian Network for Global Climate Change Research – Rede
CLIMA, and the National Institute of Science and Technology for Climate Change. Flavio Vieira ac-
knowledges financial support from CNPq. Eduardo A. Haddad acknowledges financial support from
CNPq and Fapesp; he also thanks Princeton University and Rutgers University for their hospitality.
9
10 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
The analysis heavily relied on assumptions given the paucity of empirical
studies and data in Lebanon. (. . .) Since the direct impact of climate change
on yields and crop product quality is not taken into consideration in the
agriculture census, and in research topics in Lebanon, we assumed that these
parameters vary in the same way as mentioned in the literature. (MoE 2011
p. 2.17)
Agriculture is one of the economic sectors most vulnerable to climate change as it is
directly affected by fluctuations in temperature and rainfall. Limited availability of water
and land resources in Lebanon, together with increasing urbanization, puts additional
challenges for future development in the country. In general, the direct effects of climate
on agriculture are mainly related to lower crop yields or failure owing to drought, frost,
hail, severe storms, and floods; loss of livestock in harsh winter conditions and frosts;
and, other losses owing to short-term extreme weather events. Effects of climate on
agriculture and rural areas have been extensively studied (IPCC WGII AR5 Chapter 9).
Not many studies, however, have explored the higher-order systemic impacts of climate
change on the agriculture sector within a country. Given productivity shocks that a
region may face, backward and forward linkages will affect, to different extents, the local
demand by the various economic agents. Spatial and sectoral linkages will also play an
important role in the adjustment processes. The nature and extent of the impact will
depend on the degree of exchanges with other regions. In an integrated interregional
system, there is a need to address these issues in a general equilibrium framework by also
including price effects. This broad regional view is essential to convey valuable insights
to policy makers considering integrated approaches to production value chains.
A growing body of literature exists on the assessment of systemic effects of climate
change on agriculture in the context of computable general equilibrium (CGE) models1.
Modeling strategies attempt either to include more details in the agriculture sectors
within the CGE-model structures (e.g., modeling of land use and land classes) or to in-
tegrate stand-alone models of crops yields agricultural land use with the CGE models,
usually through soft links that may use semi-iterative approaches (Palatnik and Roson
2012). Most of such CGE applications are global in nature, providing economic impacts
only at the level of regions of the world or countries. The detailed spatially disaggre-
gated information on land characteristics that may be present in land use models is lost
in aggregation procedures that are used to run the global CGE models, providing few
insights on the differential impacts within national borders.
Within this context, the objective of this study is to analyze the susceptibility of
agricultural outputs to future climate variations in Lebanon, and the extent to which it
propagates to the economic system as a whole. We use a methodological framework in
which physical and economic models are integrated for assessing the higher-order eco-
nomic impacts of projected climate changes in Lebanon in the period 2010–2030. As the
agriculture sector has important forward and backward linkages in the economic struc-
ture, as well as specific location patterns, climate change may entail economic effects for
the whole country with distinct regional impacts. On one hand, physical models of crop
yields can provide estimates of the direct impact of climate change on the quantum of
agricultural production per unit of area. On the other hand, interregional computable
general equilibrium (ICGE) models can take into account the associated productivity
changes and generate the systemic impact of projected climate variables by considering
the linkages of the agriculture sector with other sectors of the economy and the locational
impacts that emerge. Thus, assessing the economic contribution of a part of a country’s
economic sector requires some consideration of the likely paths of interactions that are
a consequence of the direct effects of climate on crop yields. Accordingly, the process
adopted here is to estimate econometrically the initial correlation between climate vari-
ables and agriculture productivity, and then to feed the results into an ICGE model to
capture the system-wide impacts of the projected climate scenarios for Lebanese regions.
We will examine how projected changes in climate variables — specifically tempera-
ture and precipitation — could impact growth and welfare in Lebanese regions through
1CGE models are based on systems of disaggregated data, consistent and comprehensive, that capture
the existing interdependence within the economy (flow of income).
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EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira 11
changes in productivity in the agriculture sector. This paper adds to the SNC in different
ways. First, it develops a quantitative study relating climatic factors to agricultural pro-
duction in Lebanon, helping to narrow one of the gaps identified in that report. Second,
it goes one step further by generating a first attempt to compute higher-order impacts
of climate change for Lebanon, despite focusing on the initial effects in only one specific
sector. Third, and most important, it quantifies the broader economic impacts consider-
ing not only the temporal dimension but also the regional disaggregation of the results.
In this regard, the paper also contributes to the literature on multiregional modeling of
the impacts of climate change.
The remainder of the paper is structured as follows: in the next section, we discuss
some of the broad features of agriculture in Lebanon. The climate scenario is then briefly
introduced, followed by a discussion of estimates of the direct effects of climate change
derived from econometric crop yields models. The next section provides an overview
of the integrated approach to derive the economy-wide impacts of the climate change
scenario in the period 2010–2030, presenting the baseline simulation and the main results
of the impact assessment. Final remarks follow.
2 The study region
Despite its small size, Lebanon presents diverse geographical features. Located on the
eastern part of the Mediterranean, it occupies an area of 10,452 km2with a coastline
nearly 220 km long. Two parallel mountain ranges running north-northeast to south-
southwest — Mount Lebanon on the west and Anti-Lebanon on the east — are separated
by the elevated upland basin of the Bekaa, the main agriculture region of the country.
The Mount Lebanon range is separated from the Mediterranean by a narrow coastal
plain, where fruits, horticulture and vegetables are the main cultivated crops (Figure 1).
Figure 1: Digital elevation model for Lebanon showing the Lebanon and Anti-Lebanon
Mountain Ranges
Lebanon’s diverse agro-ecosystems have enabled the existence of a diversified agri-
culture sector, whose main crops range from semi-tropical produce in coastal areas to
orchards in high mountains, with a wide range of different crops in between (CDR 2005).
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12 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
Topography is largely a determining factor for potential crop types and agricultural tech-
niques (see Saade 1994). Table 1 and 2 use data on crop areas to illustrate the regional
differences related to the agriculture sector in Lebanon. The tables highlight not only
the differences in the types of crops that prevail in each governorate2(table 1), but also
the main producing regions for each crop group (table 2).
Approximately half of the 270,000 hectares that are cultivated in Lebanon are ir-
rigated. Areas under cultivation are mainly concentrated in the Bekaa and Northern
Lebanon (42.1% and 27.2%, respectively), with Southern Lebanon accounting for 12.6%
and Nabatieh and Mount Lebanon accounting for 9% each (Ministry of Agriculture 2013).
In spite of this, land dedicated to agriculture has been declining over the past twenty
years, having represented nearly 18% of Lebanon’s total land in 1990, declining consid-
erably to about 13% in 1999, and further to below 11% in 2011 (World Bank 2013).
Table 1: Regional distribution of major types of crops in Lebanon (% of total crop area)
Cereals Fruit
trees Olives Industrial
crops Vegetables TOTAL
Mount Lebanon 1.0 4.2 18.9 10.0 2.2 9.5
Northern Lebanon 13.0 27.6 21.4 49.0 14.6 27.2
Bekaa 74.0 57.6 37.5 6.0 48.9 42.1
Southern Lebanon 5.0 5.6 18.3 18.0 9.3 12.6
Nabatieh 7.0 5.0 3.9 17.0 25.1 8.6
TOTAL 100.0 100.0 100.0 100.0 100.0 100.0
Source: Ministry of Agriculture
Table 2: Major types of crops distribution within regions in Lebanon (% of regional crop
area)
Cereals Fruit
trees Olives Industrial
crops Vegetables TOTAL
Mount Lebanon 2.4 8.2 63.0 25.8 0.6 100.0
Northern Lebanon 10.8 18.8 24.8 44.0 1.5 100.0
Bekaa 39.8 25.3 28.1 3.5 3.3 100.0
Southern Lebanon 9.0 8.2 45.8 34.9 2.1 100.0
Nabatieh 18.4 10.7 14.3 48.4 8.3 100.0
TOTAL 22.6 18.5 31.6 24.5 2.8 100.0
Source: Ministry of Agriculture
Although industrial crops account for about one-fourth of Lebanon’s crop area, they
represent two-thirds of agriculture output value (FAO 2014). Fruit trees account for
17% of total crop value, followed by vegetables (10%). While cereals and olives occupy
over 50% of crop areas in the country, together they represent less than 10% of the total
value of production. Overall, the agriculture sector (including livestock) is responsible
for almost 5% of Lebanon’s GDP.
3 Climate projections
Lebanon’s climate is typical of the Mediterranean region with four distinct seasons that
encompass a rainy period usually lasting from November to March, followed by a dry
period during which very little precipitation occurs. Annual precipitation on the coastal
2Administratively, Lebanon is divided into six mouhafazat (governorates). See figure A1 in the
Appendix.
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EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira 13
plain ranges between 600 mm and 800 mm. Mount Lebanon may receive up to 2000mm
of precipitation annually, but a typical range is from 1000mm to 1400 mm. Central and
northern Bekka experiences approximately 200mm to 600 mm of rainfall annually, while
for the southern portions of the plain it is 600 mm to 1000 mm (Ministry of Environ-
ment/Ecodit 2010).
In its latest assessment report, the Intergovernmental Panel on Climate Change
(IPCC) states that the frequency and intensity of drought in the Mediterranean re-
gion will likely increase into the early and late twenty-first century (IPCC 2013). The
same report predicts that precipitation in the eastern Mediterranean from the period
1986–2005 to 2081–2100 will likely decrease on average between 20% and 30%, coupled
with an increase in temperature of 2◦C to 3◦C.
According to climate predictions from the PRECIS model, by 2040 temperatures
will increase by between approximately 1◦C on Lebanon’s coast to 2◦C in its mainland;
by 2090 these temperatures will be 3.5◦C to 5◦C higher, respectively. Rainfall is also
projected to decrease by 10–20% by 2040 and by 25–45% by the year 2090, compared
with the present. This combination of significantly less precipitation and substantially
warmer conditions will result in an extended hot and dry climate. Temperature and
precipitation extremes will also intensify. The drought periods, across the whole country,
will become nine days longer by 2040 and eighteen days longer by 2090 (MoE 2011).
Table 3: Changes in temperature (Tmax, Tmin) and Precipitation (Prcp %) over Beirut,
Zahle, Daher and Cedars from the PRECIS model for winter (DJF), spring (MAM),
summer (JJA) and autumn (SON), 2025–2044
Beirut Zahle Daher Cedars
Prcp (%)
DJF -7.95 -23.50 -0.99 -1.82
MAM -8.60 35.50 -0.38 -15.50
JJA -26.80 -84.20 -39.00 -49.80
SON -8.87 23.80 14.10 12.60
Tmax (degrees C)
DJF 1.08 1.23 1.92 1.77
MAM 0.87 1.14 1.53 1.28
JJA 2.15 2.14 2.28 2.13
SON 1.48 1.64 1.67 1.70
Tmin (degrees C)
DJF 1.22 1.28 1.63 1.27
MAM 0.90 1.09 1.36 1.06
JJA 2.13 2.36 2.46 2.24
SON 1.83 2.08 1.96 1.98
Obs. As changes from 2001–2010 averages
Source: MoE (2011)
Climate change scenarios for regions in Lebanon have been developed through ap-
plication of the PRECIS model3. Details on the dynamic downscaling adopted in the
projections are provided below:
The PRECIS regional climate model (Jones et al., 2004) was applied in a
25 km x 25 km horizontal resolution whereby Eastern Mediterranean and
Lebanon particularly are at the center of the model domain, ensuring op-
timal dynamical downscaling of this region of interest. The driving emissions
scenario adopted is A1B, assuming a world with rapid economic growth, a
global population that reaches 9 billion in 2050 and then gradually declines,
and a quick spread of new and efficient technologies with a balanced emphasis
on all energy sources. PRECIS’s 25 km x 25km grid spacing is a state-of-the-
art horizontal resolution that captures the geographical features of Lebanon
3Model’s projections were made available through Lebanon’s SNC to UNFCCC, and as such we have
no control over the running of the model and any resultant or subsequent error adjustment.
REGION : Volume 1, Number 1, 2014
14 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
and resolves coastal and mountainous topographic characteristics, although
not the steep orographic gradient. The more detailed topography can be rep-
resented with even higher horizontal resolution which is still being developed
in regional climate modeling research. (MoE 2011, 1.1-1.2)
Table 3 summarizes the projections for the period 2025–2043, considering the four
different point references in the country for which information is reported in the SNC.
4 Crop yields
We have analyzed how climate variables affect the average yield of five main types of
crops: cereals, fruit trees, olives, industrial crops, and vegetables. Data limitations
constrained the specification of models that could take into account variation at the
regional level. We have relied on time series data of national crop yields and climate
variables to extract the conditional correlations of the latter with seasonal temperature
and precipitation observations for the period 1961–2001. This procedure allows the
measurement of crop yield variation (direct effects), which will be further used as a
physical measure of output change.
The empirical strategy was to define a common specification that would maximize
the use of the limited information and could be supported by the existing empirical
literature on yield effects of temperature, precipitation and technological progress. A
broader specification could also include output and input prices4. The general form of a
crop yield model using the restricted time series data set can be written as:
Yieldit =f(Climatet,Pricesi,t−1,Technologyit) + it (1)
where Yieldit represents the yield of crop iin year t;Climatetare seasonal climate
variables; Pricesi,t−1refer to the price of crop iin year t−1; Technologyit includes
information on technical progress related to crop iin year t;it is the error term. There
are many alternatives to define these variables. However, in our parsimonious approach
in which data constraints prevail, we relied on the following information. For each of
the five main types of crop, we used data for yield and prices from FAOSTAT (FAO
2014); climate variables from archives at the American University of Beirut and from the
national weather service refer to seasonal average precipitation and temperatures (max
and min). All climate variables were normalized, taking into consideration the respective
40-year sample averages. Deviations from the sample averages are meant to capture
long-term climate changes in the simulations. Note that, to maximize the use of regional
variation in the simulations, we selected the same variables for which regional climate
scenarios from the PRECIS model are provided (see table 3). The FAOSTAT database
publishes additional information that could potentially be used to identify prices of inputs
(e.g. oil price) and technology (e.g. use of fertilizers, irrigation). Given the lack of crop-
specific technology and cost information for Lebanon, we opted to identify technical
progress and aspects of the economic environment with a time trend variable (testing
also for a quadratic form). The rationale is that crop yields are expected to increase over
time because of technological advances such as the adoption of new varieties, greater
application of fertilizers and irrigation, and expansion or contraction of crop acreage.
The econometric estimates of equation (1) are presented in the Appendix. Overall,
the general specification adopted under the set of variables described above has shown a
good fit for four out of the five crops. Time trends and specific seasonal climate variables
are the main determinants of crop yields in the models.
The total direct impacts on productivity of the agriculture sector in each Lebanese
governorate were then calculated from the estimates of the crop yields models by using
Laspeyres indices whose weights were given by the shares of crops in regional output
value5. In the simulations, we have assumed that the projected scenarios of climate
change in table 3 would prevail in 2040. The accumulated effects on regional productivity
4For a review, see Huang and Khanna (2010).
5Climate projections for Beirut were associated with Mount Lebanon; Zahle with Bekaa; Daher with
Southern Lebanon and Nabatieh; and Cedars with Northern Lebanon.
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EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira 15
in the agriculture sectors in Lebanon are presented in table 4. The agriculture sector
would potentially be more affected in the southern part of the country due to the stronger
vulnerability of its crop mix (a high share of industrial crops — the most vulnerable crop
type — in the sectoral output).
Table 4: Accumulated productivity changes in the agriculture sector due to climate
change, Lebanese governorates, 2010–2030 (in percentage change)
2010–2030
Accumulated (%)
Mount Lebanon -5.72
Northern Lebanon -8.44
Bekaa -3.10
Southern Lebanon -9.66
Nabatieh -9.98
5 Higher-order impacts
Results from table 4 were translated into productivity shocks that change the produc-
tion functions of the agriculture sector in each governorate. We have assumed monotonic
changes from 2010 until the accumulated changes reached the simulated values, gener-
ating a magnification effect over time. These productivity shocks only account for the
direct impact of climate changes in the agriculture sector. As the agriculture sector is in-
tegrated with different agents in the economy, it is naturally expected that the effects on
productivity will spread to the entire economic system, generating higher-order impacts.
An ICGE model6was used to simulate the systemic impacts of changes in crop yields
by governorate, owing to climate variation. According to Haddad (2009), the general
equilibrium approach treats the economy as a system of many interrelated markets in
which the equilibrium of all variables must be determined simultaneously. Any pertur-
bation of the economic environment can be evaluated by re-computing the new set of
endogenous variables in the economy. Moreover, interregional models consider explicitly
the location of such markets. This methodological feature of general equilibrium analysis
is very attractive to our case. It allows us to define a baseline scenario that does not
incorporate climate change, and to re-estimate the model with the changes in the exoge-
nous variables that may be attributed to the expected changes in regional temperature
and precipitation, thus identifying the economic impacts associated with the changes in
climate variables.
The departure point was the ARZ model, a fully operational ICGE model calibrated
for the Lebanese economy (Haddad 2014a). The ARZ model was recently developed for
assessing regional impacts of economic policies in Lebanon. The theoretical structure
and the database of the ARZ model are documented in Haddad (2014ab).
We provide a very brief verbal description of the model’s key features, drawing on
Haddad (2014a), where the details of the model can be found. Agents’ behavior is
modeled at the regional level, accommodating variations in the structure of regional
economies. Regarding the regional setting, the main innovation in the ARZ model is the
detailed treatment of interregional trade flows in the Lebanese economy, in which the
markets of regional flows are fully specified for each origin and destination. This model
recognizes the economies of the six Lebanese governorates. The model is standardized
in its specifications, drawing on previous experiences with the MONASH-MRF and the
B-MARIA models7. Results are based on a bottom-up approach — i.e. national results
are obtained from the aggregation of regional results. The model identifies eight produc-
tion/investment sectors in each region producing eight commodities, one representative
6Reviews of ICGE models are found in Partridge and Rickman (1998), and Haddad (2009).
7Peter et al. (1996) and Haddad (1999).
REGION : Volume 1, Number 1, 2014
16 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
household in each region, one government, and a single foreign area that trades with each
domestic region. Two local primary factors are used in the production process, according
to regional endowments (capital and labor). Special groups of equations define capital
accumulation relations. The model is structurally calibrated for 2004–2005; a compre-
hensive data set is available for 2005, of which the last national input-output tables —
that served as the basis for the estimation of the interregional input-output database —
were published. Additional structural data from the period 2004–2005 complemented
the database8.
In order to examine the higher-order effects of changes in productivity in agriculture
related to climate change projections, we conducted two sets of simulations, following
standard procedures described in Giesecke and Madden (2006). The first set of simula-
tions is undertaken to produce a baseline forecast for the Lebanese economy for the period
of 2010 to 2030. These ARZ forecasts incorporate information on trends in sectoral TFP
growth, forecasts of commodity prices and growth of the world economy, estimates of re-
gional population growth, and trends in sectoral investments. Using this information, the
model generated forecasts for a wide range of variables (see table A2 in the Appendix).
We repeated our forecasts under the assumption that the productivity in agriculture
would grow slower over the period to 2030. This involved the same set of shocks imposed
to generate the baseline forecast, plus an additional set of shocks that incorporate the
direct effects of the slower productivity growth. The new forecasts were then compared
with the baseline forecasts. Results are reported as deviations (in either change or per-
centage change terms) of the lower productivity growth scenario for 2010 to 2030 from
the baseline forecasts. Thus, the results show the effects on the economy of a scenario
in which the productivity of the agriculture sector grows at a slower rate than under a
“business as usual” scenario.
One difference between the two closures (baseline and “policy”) is that we have
swapped the regional population growth variable (exogenous in the baseline) with the re-
gional utility change variable (endogenous in the baseline). Thus, the population change
impact reported below should be interpreted as the population movements necessary to
keep the baseline utility levels unchanged in the regions.
Tables 5 through 7 present results for selected macroeconomic, industry and regional
variables. The accumulated results presented in the last two columns of table 5 are simply
the sum of the annual marginal flows related to the differences between the two scenarios,
shown in LBP and percentage of the baseline values in 2010. In order to calculate annual
GDP losses that are accrued until 2030 at their present value, taking into account the
value of time, three different discount rates were used: 0.5%, 1% and 3% per year (table
6).
Regarding the impacts of climate change on the economy through changes in crop
yields, the simulations revealed a permanent loss for Lebanon GDP by 2030 of approxi-
mately 0.55% when the scenarios with and without climate change are compared.
Present values of losses range between 5.50% and 7.75% of the GDP for 2010. There-
fore, if the costs from climate change in Lebanon by 2030 were brought forward to today,
at an intertemporal discount rate — for example — of 1.0% per year, the cost in terms
of the GDP would be LBP 4,140 billion, which would account for 7.22% of the GDP for
2010. In terms of welfare, the average Lebanese citizen would lose around LBP 504,000
(US$ 336) in terms of the present value of the reductions in household consumption
accumulated to 2030, representing 4% of current per capita annual consumption.
These economic impacts would be experienced in different ways across the sectors and
regions. For example, agriculture would be the sector most directly sensitive to climate,
with a permanent decline in production of LBP 105.9 billion by 2030, which is equivalent
to 1.9% of the baseline sectoral value added at that year. The total accumulated losses
in the period would account for almost half of the sectoral GDP for 2010 (without taking
into account any discount factor over time).
From the regional perspective, the greatest threat exists for the poorest regions in
the country. It is fair to conclude from GRP results in table 5 that the effects of climate
change on crop yields will potentially exacerbate regional inequalities in Lebanon. The
8See Haddad (2014b) for a detailed description of the database.
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EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira 17
Table 5: Systemic impacts of productivity changes in agriculture due to climate change on selected variables (deviations from base case)
2010 2015 2020 2025 2030 2010–2030
Accumulated % of 2010 values
Macroeconomic indicators (Billions LBP 2010)
GDP -28.6 -110.7 -228.7 -401.7 -522.0 -4770.9 -8.33%
Household consumption -22.3 -75.0 -132.0 -200.0 -223.6 -2457.4 -4.85%
Government expenditure -3.0 -8.2 -12.6 -17.4 -17.9 -221.3 -3.12%
Investment 1.4 -5.0 -36.1 -103.6 -181.7 -1129.0 -7.94%
Exports of goods and services -8.3 -29.8 -55.6 -86.1 -95.7 -1041.2 -5.01%
Imports of goods and services 3.5 7.3 7.7 5.4 -3.2 77.9 -0.22%
Sectoral value added (Billions LBP 2010)
Agriculture -6.2 -29.3 -62.2 -97.8 -105.9 -1169.1 -47.59%
Manufacturing -2.1 -7.1 -12.9 -20.0 -22.2 -241.3 -4.80%
Services -20.3 -74.4 -153.6 -284.0 -394.0 -3360.5 -6.75%
Gross Regional Product (Billions LBP 2010)
Beirut -2.8 -29.3 -62.2 -97.8 -105.9 -246.4 -3.24%
Mount Lebanon -9.8 0.5 1.3 2.3 2.7 -1097.5 -4.32%
Northern Lebanon -6.3 -7.1 -12.9 -20.0 -22.2 -1262.2 -12.33%
Bekaa -3.6 -26.3 -72.3 -166.9 -274.2 -680.6 -11.15%
Southern Lebanon -3.2 -3.3 -5.6 -8.4 -8.9 -807.2 -16.26%
Nabatieh -2.9 -26.7 -46.1 -69.3 -75.4 -677.1 -22.64%
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18 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
Table 6: Present value of marginal flows associated to the impacts of productivity changes
in agriculture due to climate change, 2010–2030
Discount rate
0.5 1.0 3.0
GDP (LBP billion 2010) -4,442.2 -4,139.8 -3,150.5
GDP (% of 2010 value) -7.75% -7.22% -5.50%
Per capita HH consumption (LBP 2010) -538,873 -504,412 -391,022
Per capita HH consumption (% of 2010 value) -4.28% -4.00% -3.10%
Table 7: Systemic impacts of productivity changes in agriculture due to climate change
on regional population (net migrants)
2010–2030
Accumulated % of 2010 values
LEBANON -128,336 -3.19%
Beirut -18,137 -4.28%
Mount Lebanon -52,798 -3.27%
Northern Lebanon -21,772 -2.65%
Bekaa -14,863 -2.94%
Southern Lebanon -13,698 -3.22%
Nabatieh -7,069 -3.07%
Figure 2: Regional impacts of productivity changes in agriculture due to climate change
on GRP (% deviations from baseline)
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EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira 19
most significant discrepancy can be found by comparing the systemic effects of climate
change in Nabatieh and Southern Lebanon (accumulated losses in relation to the 2010
baseline’s values of 22.64% and 16.26% by 2030, respectively) to the effects in Beirut and
Mount Lebanon (losses of 3.24% and 4.32%, respectively). Moreover, as we analyze the
annual GRP impacts as deviations from the baseline, we notice that regional inequality
is potentially magnified over time (figure 2).
A final point refers to regional welfare, as suggested by the results on net migration
presented in table 7. Those estimates take into account endogenous population changes
in order to maintain the baseline utility levels in the regions. The higher percentage
changes in the population in Beirut and Mount Lebanon, required to keep residents as
well off as in the baseline (no climate change), reveal important impacts on relative
changes in the cost of living in the central areas of the country. This negative effect,
common to all governorates, would be mostly due to the reduction in real income caused
by the general increase in prices led by the increase in the prices of agricultural products.
6 Final remarks
The SNC has identified several gaps related to the assessment of vulnerability and impact
of climate change on agricultural crops in Lebanon. Ways in which this has been achieved
range from the use of a more accurate climate model, to the exhaustive application of GIS
techniques to improve information available for agronomic variables (MoE 2011, 2.61).
Accordingly, the assessment could have better invested into GIS techniques in order to
strengthen the results and minimize assumptions. However, the limited availability of
data and maps, in addition to time constraints, hindered the use of such tools (Ibid,
2.17).
We do recognize that, at this stage, there are still data limitations. However, do
we wait until the data have improved sufficiently, or do we start with existing data, no
matter how imperfect, and improve the database gradually? In this paper, we have opted
for the latter, following the advice by Ag´enor et al. (2007) for approaches to quantitative
modeling in developing economies.
With renewed interest by policymakers on regional issues in Lebanon after the publi-
cation of the National Physical Master Plan of the Lebanese Territory — NPMPLT (CDR
2005), the notion that there is little interest in spatial development planning and spa-
tial development issues in small size countries has been challenged (Haddad 2014a). The
NPMPLT has identified challenges for the future economic development of the country in
different sectors in a context of increasing internal and external obstacles to the Lebanese
economy. Climate change poses additional uncertainty to the future of Lebanese regions.
Our study of the economic impacts from climate change on crop yields in Lebanon, de-
spite its limitations, shows that there are potential high costs and risks associated with
a burden to the poorer and more vulnerable regions of the country.
The great methodological challenge remains to establish a link between future climate
projections and business sectors and several environmental and socio-economic features
at local and regional levels. Additionally, a level of aggregation or disaggregation of
analyses that makes research in this area relevant and a faithful reflection of the “local”
reality at a minimum must be established, and it must be feasible from the perspective
of information and data handling. This is a critical issue in studies involving a myriad
of interconnected economic agents with different natures. The deterministic approach of
our study, for instance, is just one of key limitations. We have explicitly omitted the risk
and uncertainty by emphasizing expected average values9. Regional science has a central
role to play in helping to narrow these gaps. There is plenty of existing experience with
large-scale multi-regional and multi-sectoral models, including uncertainty and ways to
handle it, from which the scientific community can learn to apply in interdisciplinary
studies.
9As emphasized by one of the referees, there is a degree of uncertainty in the results of the climate
models and thus the uncertainty within climate change further compounds the uncertainty of climate
change impacts.
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20 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
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EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira 21
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22 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
Appendix
Figure A1: Lebanese governorates and their population
Source: CAS, 2013
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EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira 23
Table A1: Econometric estimates
Variables/Productivity prd cereals prd fruit prd olive prd indus prd veget
time 0.0244 0.5559 0.0384 0.1484 0.2186
(0.037) (0.000) (0.117) (0.145) (0.011)
time2 -0.0131
(0.002)
p cereals1 0.0078
(0.582)
p fruit1 0.0386
(0.184)
p olive1 2.2395
(0.109)
p indus1 -0.0020
(0.009)
p veget1 -0.1250
(0.000)
winter n -0.0577 -0.6053 0.2251 -0.3244 0.1010
(0.315) (0.017) (0.072) (0.509) (0.805)
spring n 0.0702 0.4372 0.0089 -0.5671 0.2468
(0.258) (0.104) (0.944) (0.279) (0.562)
summer n -0.0072 -0.0474 -0.1113 0.3504 -0.4723
(0.903) (0.851) (0.376) (0.485) (0.252)
fall n 0.0513 0.5917 -0.1108 0.2718 -1.2008
(0.392) (0.041) (0.376) (0.598) (0.006)
winter tem max n -0.1400 -0.8466 0.1530 -0.0264 -0.4372
(0.367) (0.210) (0.644) (0.984) (0.700)
spring tem max n 0.1344 0.1275 0.2447 -1.8785 -1.7605
(0.263) (0.801) (0.330) (0.068) (0.042)
summer tem max n -0.0114 -0.2612 -0.0399 -0.6323 -0.0561
(0.891) (0.452) (0.822) (0.376) (0.927)
fall tem max n -0.0585 0.5741 -0.0824 1.3437 -0.4552
(0.557) (0.174) (0.691) (0.116) (0.502)
winter tem min n 0.0302 -0.2286 -0.4641 -0.1710 0.6329
(0.876) (0.781) (0.267) (0.918) (0.656)
spring tem min n -0.1542 -0.2203 -0.6467 0.9328 2.2449
(0.381) (0.764) (0.093) (0.530) (0.075)
summer tem min n 0.2573 1.0862 0.4560 0.9984 1.2984
(0.098) (0.148) (0.165) (0.438) (0.222)
fall tem min n 0.1676 -0.1608 0.1076 -1.2030 0.4263
(0.216) (0.783) (0.702) (0.305) (0.643)
constant -0.0086 -8.4601 -2151.58 18.8068 57.1251
(0.781) (0.508) (0.109) (0.000) (0.000)
R-Squared 0.8334 0.8267 0.4880 0.8747 0.9237
Note: p-value in parenthesis
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24 EA Haddad, N Farajalla, M Camargo, RL Lopes, F VVieira
Table A2: Baseline indicators, Lebanon 2010–2030
2010 2015 2020 2025 2030
Average
annual growth
2010–2030
Macroeconomic indicators
(Billions LBP 2010)
GDP 57,299 67,847 78,839 87,454 94,324 2.52
Household consumption 50,657 59,920 68,723 75,473 80,873 2.37
Government expenditure 7,083 7,237 7,401 7,483 7,531 0.31
Investment 14,226 14,577 15,840 16,909 17,803 1.13
Exports goods & services 20,777 25,189 29,055 31,936 34,042 2.50
Imports goods & services -35,444 -39,077 -42,179 -44,346 -45,925 1.30
Sectoral value added
(Billions LBP 2010)
Agriculture 2,456 3,299 4,151 4,911 5,577 4.19
Manufacturing 5,022 6,095 7,191 7,982 8,535 2.69
Services 49,821 58,453 67,497 74,561 80,212 2.41
Gross Regional Product
(Billions LBP 2010)
Beirut 7,608 8,946 10,333 11,426 12,313 2.44
Mount Lebanon 25,398 30,288 35,254 39,122 42,197 2.57
Northern Lebanon 10,239 12,020 14,006 15,630 17,002 2.57
Bekaa 6,102 7,207 8,372 9,262 9,929 2.46
Southern Lebanon 4,963 5,883 6,821 7,517 8,015 2.43
Nabatieh 2,990 3,503 4,053 4,497 4,869 2.47
Population
LEBANON 4,021,367 4,158,521 4,252,732 4,300,625 4,328,435 0.37
Beirut 423,613 442,500 454,292 461,353 466,629 0.48
Mount Lebanon 1,613,325 1,675,291 1,711,517 1,729,116 1,739,156 0.38
Northern Lebanon 822,745 836,638 855,451 864,840 869,815 0.28
Bekaa 505,370 520,992 532,262 537,632 540,217 0.33
Southern Lebanon 426,033 443,626 454,262 459,953 463,361 0.42
Nabatieh 230,280 239,474 244,948 247,731 249,258 0.40
Per capita GDP
(Thousands LBP 2010)
LEBANON 14,249 16,315 18,538 20,335 21,792 2.15
Beirut 17,960 20,218 22,744 24,767 26,386 1.94
Mount Lebanon 15,742 18,079 20,598 22,626 24,263 2.19
Northern Lebanon 12,445 14,367 16,372 18,073 19,546 2.28
Bekaa 12,074 13,833 15,730 17,227 18,380 2.12
Southern Lebanon 11,649 13,260 15,017 16,342 17,297 2.00
Nabatieh 12,986 14,627 16,545 18,153 19,534 2.06
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