Assessment of Climate Change Impacts on Electricity Consumption and Residential Water Use in Cyprus Theodoros Zachariadis Dept. of Environmental Science.
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Assessment of Climate Change Impacts on Electricity Consumption and Residential Water Use in Cyprus Theodoros Zachariadis Dept. of Environmental Science & Technology Cyprus University of Technology tel. 25 002304, e-mail: email@example.com November 2011
Analysis of Climate Effects on Energy Use Econometric time series analysis of energy use in Cyprus by sector and fuel, 1960-2007 Energy consumption = f (income/economic activity, energy prices, time trends, weather) Climate effects captured by the variables of heating & cooling degree days (they express intensity + duration of cool & hot days respectively) Methodology: stationarity (unit root) tests of all variables, application of cointegration techniques, Vector Error Correction Models and Autoregressive Distributed Lag Models Effect of climate statistically significant only for electricity consumption in households & tertiary sector
Forecast of electricity consumption up to 2030 – without climate change Electricity use triples by 2030 Increased share of domestic & tertiary sector – 86% in total
Forecast of electricity consumption up to 2030 – with climate change Assumption: uniform temperature increase by 1 C in 2030, during the whole year Electricity use in 2030 higher by 2.9% (compared to ‘no climate change’ scenario) Direct cost: 15 ΜEuros in 2020, 45 MEuros in 2030 Present value of total cost in period 2008-2030: > 200 MEuros (at constant prices of year 2007) Average cost per household: ~30 Euros/year in 2020, ~80 Euros/year in 2030 (at constant prices of year 2007) Further econometric analysis + forecast of peak electricity load in summer with climate change: additional 65–75 MW in 2020, 85–95 MW in 2030 Increased requirements for extra reserve capacity
Work in progress Updated electricity consumption forecasts for 2030 New forecasts for year 2050 –with new macroeconomic & price assumptions –using recent climate change forecasts (Hadjinicolaou et al. Regional Environmental Change, pp. 1-17, 10/2010)
Methodology to assess costs of water shortages in non-agricultural sectors Willingness to pay for water p (€/c.m.) Price Water demand curve q0q0 q'q' Water quantity q Welfare losses of consumers due to reduced availability of water
Estimating Residential Water Demand in Cyprus Data from the three Water Boards of Cyprus serving the main cities (Nicosia, Limassol, Larnaca): –Billed water consumption per consumer type (residential, commercial, industrial) –No. of consumers by type –Water tariffs (fixed prices & prices per consumption block) –Fraction of consumers in each consumption block –Revenues and expenditures (from Board financial accounts) –Period: 1980-2009 (annual data), 2000-2009 (data available per billing period – 2/3/4 months) Other data: –Monthly temperature and rainfall (from Met. Service) –Quarterly GDP & population (from Statistical Service) –Household income by district of Cyprus (Family Expenditure Surveys conducted by Statistical Service)
Residential Water Demand Model q it = f (inc it, p it, pfix it, temp it, rain it, dummy i ) i : district (i = 1 to 3); t : 4-month period from 2000/1 to 2009/3 q : water consumption per household (c.m.) inc : household income (€) p : water price (€/c.m.) pfix : fixed part of water tariff (€) temp, rain : temperature & rainfall level ( C, mm) dummy : for the period of interruptions in water supply in each city (April 2008 – December 2009) Linear function, variables in logarithms, each variable’s coefficient expresses an elasticity Two models: a) p = average price, b) p = marginal price
Econometric Estimation Typical problem with block pricing: endogeneity of prices – each consumer faces a water price that depends on the quantity consumed. Usual estimation method (OLS) will be biased Two-Stage Least Squares (2SLS) Estimation is appropriate Requires identifying instruments that correlate with price but not with dependent variable (water consumption) Three instruments were used: a) Consumer Price Index of previous year b) Water Board expenditures in previous year per c.m. of water sold c) Same expenditures in current year
Estimation Results Average price model Coefficients of: Income 0.529 Avg. price-0.248 Fixed tariff-0.441 Temperature 0.241 Rainfall 0.047 Dummies: Nicosia-0.034 Larnaca 0.065 Limassol-0.193 Marginal price model Coefficients of: Income 0.753 Marg. price-0.449 Fixed tariff-0.490 Temperature 0.292 Rainfall 0.061 Dummies: Nicosia-0.025 Larnaca 0.088 Limassol-0.253 N = 73 Coefficients in bold are statistically significant at 1% level
An adaptation measure: ‘Efficient’ household water prices to account for scarcity Climate change increases water shortages modestly, requires 8-13 €cents/c.m. higher water prices to induce conservation in order to address this additional scarcity
An adaptation measure: Effects of ‘efficient’ household water pricing
An adaptation measure: What if we had ‘efficient’ water pricing already in 2000?