Evaluating Impact of Climate Change to Fishing Productivity of Vietnam: An Application of Autoregressive Distributed Lag (ARDL) Regression Model45226

Evaluating Impact of Climate Change to Fishing Productivity of Vietnam: An Application of Autoregressive Distributed Lag (ARDL) Regression Model Nguyen Thi Vinh Ha(1)* (1) VNU University of Economics and Business, Vietnam National University, Hanoi, Vietnam * Correspondence: vinhha78@gmail.com Abstract: Fisheries are most affected by climate change Yet studies on impact of climate change to fisheries in Vietnam are still limited This paper uses production function with Autoregressive Distributed Lag (ARDL) regression and finds out that fishing productivity in Vietnam is negatively impacted by climate change In the long run, if sea surface temperature increases by oC, fishing productivity, as measured by catch per unit effort (CPUE), will decrease by 0.25 ton/CV However, CPUE does not statistically significantly alter if there are changes in precipitation and number of storms Keywords: fishing productivity; climate change; impact assessment; Autoregressive Distributed Lag (ARDL) regression INTRODUCTION Climate change is having profound effects on environment, natural resources and economic, political and social life of economies around the world Fisheries are most affected by climate change (Williams and Rota 2010) The world's fish stock has been significantly affected by various impacts such as overfishing, pollution, loss of habitats, declining biodiversity, epidemics, etc (Brander 2010) Climate change exacerbates and has faster direct and indirect effects on aquatic species The impacts of climate change to the oceans include increasing sea temperature, reducing dissolved oxygen, changing salinity, falling pH, and shifting ocean currents, etc (Brander 2010; Sumaila et al 2011) These changes positively or negatively affect growth, reproductive productivity, behaviors and distribution of marine species (Brander 2010; Pinnegar et al 2013; Sumaila et al 2011) Aquatic invertebrates and fishes are thermoplastic, each species has different tolerance to temperature (Williams and Rota 2010) Rising temperature reduces the ability of dissolving oxygen in water, restricting respiration and affecting the health of aquatic species Therefore, when the water is warmer, fish will move to cooler areas for their favorite condition Aquatic species also prefer different salinity levels The alteration of seawater salinity due to climate change is not significant so far However, in the future, the salinity of the oceans is likely to upsurge due to the increasing amount of groundwater containing salt running into the sea In the polar regions, salinity may fall due to augmented rainfall and low-salinity water flow from rivers (Roessig et al 2004) The indirect impacts of climate change via ecosystems include foods, competitors, predators and pathogens of aquatic species (Brander 2010) The ocean acidification makes marine creatures like mollusks, zooplankton, etc difficult to create shells This disturbs the food webs, thereby shifting species distribution, growth and structure, leading to influences on organisms in the oceans, estuaries, coral reefs, mangroves, and seagrass beds which are habitats of fishes Under-optimal environmental conditions can lessen feed intake, foster competition for food and shelter These changes deteriorate rate of growth, reproduction, behavior, distribution, species structure, stocks, migration behavior, and survival of aquatic species The likelihood of fish disease also worse due to rise of temperature, sea level, storms and cyclones (Brander 2010; Roessig et al 2004) According to Roessig et al (2004) and Cheung et al (2009), climate change negatively affect fisheries in low latitude waters while it increases fishing benefits in high latitude waters However, fisheries in both sea areas can be negatively affected by the deterioration of water quality and the increasing likelihood of disease (Williams and Rota 2010) Vietnam locates in tropical region, its fishing industry is therefore more likely to be negatively affected by climate change This paper uses production function model with time series data to assess impact of climate change on fishing productivity in Vietnam The production function approach has been widely used in research on assessment of effects of changes in environmental quality The Economic Commission for Latin America and the Caribbean (ECLAC) (Kirton 2011) assessed the relationship between fishery production (both capture and aquaculture) and seafood export price, sea surface temperature and annual average rainfall The results showed that the sea surface temperature and the average rainfall were inversely proportional to the fishery production in Guyana Damage to the fishery sector to year 2050, under the A2 (high emission) scenario, was estimated at USD 15 million (4% social discount per annum) For the B2 (medium emission) scenario, the anticipated damage by 2050 was 12 million USD at 4% discount Caviedes and Fik (1992) showed that during the El Nino period 1997-1998, the pelagic fish catch yield in Peru and Chile decreased by 50% and 52% respectively, leading to declines in export values and negative economic impacts (losses of job and income) in both countries Catch per unit effort was modeled to depend on annual average sea surface temperature and El Nino events MARMA regression was applied to correct time series problems such as autocorrelation and non-stationarity Aaheim and Sygna (2000) used time series from 1980 to 1998 to examine the impact of El Nino and La Nina on tuna catches in Fiji and Kiribati The study showed that the Southern Oscillation did not significantly affect tuna catch in Fiji while catch increased with El Nino in Kiribati However, Aaheim and Sygna (2000) acknowledged that the regression model was so simple that it might not produce good estimation results In Vietnam, Pham et al (2012) studied impacts of climate change on shrimp production in seven ecological regions of Vietnam Research results showed that there was no correlation between shrimp productivity and temperature between 1990 and 2009 and seasonal rainfall from 1995 to 2009 in Phu Tho and Hoa Binh provinces In the North Central Coastal region, temperature had an impact while rainfall had no influence on shrimp production in Nghe An and Thua Thien Hue provinces Cao et al (2013) quantified the variation in shrimp production due to changes in temperature and rainfalls in Thanh Hoa and Ha Tinh provinces They found out that there was inverse correlation between shrimp production with temperature and precipitation, in addition to capital, labour and acreage of shrimp ponds Nguyen et al (2015) forecasted the impact of climate change on fisheries production in northern region of Vietnam The study showed that the total damage of fishing industry in 2050 would be 584 billion VND at social discount rate of 3% in the medium (RCP4.5) climate change scenario PRODUCTION FUNCTION MODEL AND DATA Production function decribes relationship between inputs and outputs of a production process In theory, there are two major inputs of production which are capital and labor In sectors such as agriculture or fisheries, climate could be considered as an additional input Impacts of climate change are measured as differences of outputs as results of variations in climate factors Let Y denote production output, K capital, L labour, and CC climate factor, the production function is expressed as formula (1) Y = f (K, L, CC) (1) If climate change has impact on output, then δY/δCC is different to zero Adjusted Cobb-Douglas function (Zellner et al 1966) is chosen for model specification and the production function has formula (2) Y = A.Kα.Lβ.CCγ (2) In which α, β and γ are elasticities of output to capital, labour and climate factors respectively A is the impact of other factors The logarithm of the two sides are applied to have the formula (3) LnY = LnA + αLnK + βLnL + γLnCC (3) In this study, output of fisheries sector is measured in terms of fishing productivity, represented by catch per unit effort (CPUE) Fishing productivity, as measured by catch per unit effort (CPUE) might be used as proxy to fish stock Stable CPUE shows sustainable catch yield while decreasing CPUE means that fish is over-exploited (Quirijns et al 2008) Since there is no available data on investment in fisheries sector, the study uses the variable of total fishing vessel capacity (measured in horsepower, or cheval vapeur - CV) as a proxy to capital In Vietnam, climate change manifests in increasing temperature and precipitation, which may have influence on fish growth and migration behavior, affecting the fish stocks and then catches Storms (with windspeed of level or higher) cause damages to the fisheries activities and fishing vessels, losses of life and property, and deteriorating livelihoods of fishing communities According to Ngo et al (2013), wind speed from level to level is convenient for fishing activities at sea El Nino and La Nina, which perform similarly to climate change in short run, also have some bearing on fishing So, variables on temperature, precipitation, storms and El Nino are included in the production function In 1997, the Vietnamese Government encouraged fishermen to invest in offshore fishing via a preferential finance project (Decision No 393-TTg dated 09 June 1997) In 2003, the National Assembly promulgated the Law of Fisheries After these two milestones, there were major policy changes related to fishing activities in Vietnam and supposed to have positive effects on the catch yields Therefore, we add two dummies to assess the impact of these policies in the production model The production function has the following form: CPUEt = β0 + β1LnCapacityt + β2LnLabourt + β3SSTt + β4LnRainfallt + β5Typhoont + β6SOIt + β7D1+ β8D2 + εt (4) Data and sources of data for the regression models are described in Table Table 1: Data description Variables Description Sources T Time in year From 1976 to 2014 Catcht Catch yield in year t 1976 - 2010: Ngo et al (2013) 2011 - 2014: General Statistical Office (GSO) (2016) Capacityt Catch effort in year t (CV) 1976 - 2010: Ngo et al (2013) 2011 - 2014: GSO (2016) CPUEt Catch per unit effort in year t (tons) CPUEt = Catcht/Capacityt Labourt Total fishing labor in year t (persons) 1976 - 2010: Ngo et al (2013) Missing values (in 1978, 2011 - 2014) are filled by interpolation SSTt Average sea surface temperature in year t (°C) The National Oceanic and Atmospheric Administration (NOAA), USA Rainfallt Total precipitation in year t (mm) Climate Change Knowledge Portal, the World Bank SOI Southern Oscillation Index NOAA Notes Including marine and inland catch Fishing productivity Measured at Halong Bay Difference in air pressure between Tahiti (Southern Pacific) and Darwin (North to Australia) Typhoont Number of typhoons in year t National Centre for Hydro-Meteorological Forecasting (Vietnam) Dinh (2010) D1 Proxied to offshore fishing finance project in 1993 Value for years before 1997, value for years 1997 and later D2 Proxied to the availability of Law of Fisheries Value for years before 2003, value for years 2003 and later LnX Logarithm of X βi Coefficients εt White noise Number of storms in the Eastern Sea Autoregressive Distributed Lag regression In addition to the fishing inputs and other impacting factors of the same year, annual fishing productivity tends to rely on productivity and factors of previous years due to lagging impacts Therefore, the Autoregressive Distributed Lag (ARDL) model (Pesaran and Shin 1998) is chosen to demonstrate these dependencies ARDL is ordinary least square (OLS) regression, which includes the lagged variables of dependent and independent variables The ARDL model is appropriate when time series have different degrees of integration (e.g I (0), I (1) or a combination of both) and especially when there is a single long-run relationship among variables It is also suitable with small sample size (n ≤ 30) (Nkoro and Uko 2016) ARDL model has the following form: Yt = c + α1Yt-1 + α2Yt-2 +… + αpYt-p + β0Xt + β1Xt-1 + … + βqXt-q + ut (5) In which Y is dependent variable, X are explanatory variables, α and β are coefficients, p and q are number of lags of dependent and explanatory variables respectively, c is intercept, t denotes time and ut is white noise Several tests should be performed to confirm the appropriateness of the ARDL model, including selection of number of lags, tests for stationarity, long-run relationship among variables, model specification, autocorrelation, heteroscedasticity, multicollinearity, white noise (i.e residual series are normal distribution and stationary), stability of the coefficients and convergence of long-run coefficients Test for stationarity of time series Normally, time series regressions require all series to be stationary, i.e mean, variance, and covariance at different lags have constant values over time (Gujarati and Porter 2009) Non-stationary series can lead to spurious regression However, according to Nkoro and Uko (2016), ARDL regression is suitable with integrated time series of order zero or one Augmented Dickey-Fuller (ADF) test and Schwarz information criteria (SIC) are applied to perform unit root tests of the time series The test results (Table 2) show that CPUE and LnLabour are integrated of order They are difference stationary LnCapacity is integrated of order zero, which is trend stationary, or its mean trends are deterministic The other time series are integrated of order zero Therefore, while traditional OLS regression is not applicable to this data set, ARDL regression can work Table 2: Unit root test results Variable ADF statistics p-value p-value of difference Critical value 1% 5% 10% CPUE -1.933 0.618 0.072 -4.219 -3.533 -3.198 D(CPUE) -6.277 0.000 - -3.621 -2.943 -2.610 LnCapacity -3.599 0.043 0.000 -4.219 -3.533 -3.198 LnLabour -2.467 0.342 0.008 -4.260 -3.548 -3.209 D(LnLabour) -4.276 0.002 - -3.621 -2.943 -2.610 SST -3.862 0.005 - -3.616 -2.941 -2.609 LnRainfall -5.447 0.000 - -3.616 -2.941 -2.609 Typhoon -5.039 0.000 - -3.616 -2.941 -2.609 SOI -4.358 0.001 - -3.616 -2.941 -2.609 In which D(x) denotes the first-order difference of x, i.e D(CPUEt) = CPUE t - CPUE t-1 Selecting number of lags for regression models Vector autoregression (VAR) test and Akaike information criterion (AIC) are applied to select number of lags in ARDL model The VAR results (Table 3) show that the ARDL should have lags for all variables Table 3: Selection of optimal number of lags using VAR Lag LogL LR FPE AIC SIC HQ 0 -98.51 - 8.29e-07 5.86 6.17 1 68.02 259.04* 1.28e-09 -0.67 1.80* 2 116.54 56.61 1.81e-09 -0.64 3.98 3 191.46 58.27 1.19e-09* -2.08* 4.69 *Number of lags selected by criteria Test for long-run relationship among variables To seek for the existence of long-run relationship among variables, the bound test is performed, using bound F-statistics and t-statistics to determine the cointegration among variables (Nkoro and Uko 2016) Bound test has the following form: ∆Yt = δ0 + + + δ1∆Yt-1 + δ2∆Xt-1 + vt (6) In which ∆ denotes the difference values, for example ∆Yt = Yt - Yt-1; p is the maximum lag chosen by author; (δ1 - δ2) depicts the long-run relationship, while (α1 - α2) depicts short- run one Wald test is applied with null hypothesis is that all coefficients of lagged variables are zero F-statistics in Wald test does not follow normal distribution It depends on: (1) integration orders of variables (I(0) or I(1)); (2) number of independent variables; (3) the existence of constant and trend variables in the model; and (4) sample size (Narayan 2005) Narayan (2005) provided critical values for ARDL model with small sample size (from 30 to 80 observations) If the F-statistics is larger than the upper bound of the critical value, the null hypothesis is rejected Results of bound test are described in Table F-statistics in model with dummies D1 and D2 is 7.4652, larger than the critical value 5.797 at significance level of 1% F-statistics in model without dummies is 5.1777, larger than the critical value 4.324 at significance level of 5% The t-statistics in model with dummies is -5.3166, less than the critical value -4.99 at significance level of 1% The t-statistics in model without dummies is -4.5429, less than the critical value -4.38 at significance level of 5% Therefore, we reject the null hypothesis and accept that there is long-run relationship among variables in the models The application of ARDL regression is acceptable with the data set Table 4: Bound tests Test statistics Value Significance level I(0) I(1) Sample size n = 40 F-statistics 5% 2.797 4.211 1% 3.800 5.643 With dummies 7.4652 Without dummies 5.1777 Number of degrees k 5% 2.864 4.324 Real sample size 36 1% 4.016 5.797 With dummies -5.3166 5% -2.86 -4.38 Without dummies -4.5420 1% -3.43 -4.99 Sample size n = 35 t-statistics Test for autocorrelation Table 5: Breusch-Godfrey test n*R Chi With dummies Without dummies 21.623 21.344 0.000 0.000 Breusch-Godfrey test is applied to seek for autocorrelation It gives p-values of Chi square at 0.000 for both models (Table 5) So, we reject the null hypothesis and accept that the models have autocorrelation For OLS regression, when there is autocorrelation, the estimates are not biased but ineffective (non-smallest variance), leading to unreliable F and t tests Newey-West estimates are used as a remedy The correlograms after the Newey-West estimation show that the models are no longer autocorrelated Autocorrelation Autocorrelation With dummies Without dummies Figure 1: Correlograms Test for suitability of model specification Table 6: Ramsey Reset test for the suitability of model specification With dummies Without dummies F-statistics 5.5179 4.3497 Degree of freedom df (3.3) (1.7) p-value 0.0972 0.0775 p-value of the models are larger than α=0.05, so the model specification is suitable Test for heteroskedasticity Table 7: Breusch-Pagan-Godfrey test for heteroskedasticity With dummies Without dummies n*R2 29.7270 26.3448 p-value of Chi square 0.4277 0.4995 p-value of Chi square is larger than α=0.05, we accept the hypothesis that there is no heteroskedasticity in models Harvey and Glejser tests provide the same results Test for multicollinearity Most of explanatory variables of the models are statistically different from zero, Durbin-Watson d statistics is close to 2, so we can ignore the multicollinearity in the models Test for stability of the coefficients Cumulative sum (CUSUM) and cumulative sum square (CUSUMSQ) control charts (Figure 3) show that CUSUM and CUSUMSQ curves lie between the critical curves at significance level of 5% This result confirms that there is long-run relationship among variables and the coefficients are stable 8 1.6 1.2 0.8 0.4 -2 -4 0.0 -6 -8 -0.4 2009 2010 2011 CUSUM 2012 2013 2014 2009 2010 5% S ignificance 2011 2012 CUSUM of Squares 2013 2014 5% Significance With dummies 10.0 1.6 7.5 1.2 5.0 2.5 0.8 0.0 0.4 -2.5 -5.0 0.0 -7.5 -10.0 -0.4 2007 2008 2009 2010 2011 CUSUM 2012 2013 2014 2007 2008 5% Significance 2009 2010 2011 CUSUM of Squares 2012 2013 2014 5% Significance Without dummies Figure 1: CUSUM and CUSUMSQ control charts Test for normal distribution of residuals Using the Jarque-Bera test on the residual series of the models gives the p-values of all models greater than α = 0.05 We accept the hypothesis that the residual series follow normal distribution (Figure 2) Series: Residuals Sample 1979 2014 Observations 36 Series: Residuals Sample 1979 2014 Observations 36 5 Mean Median Maximum Minimum Std Dev Skewness Kurtosis 8.88e-16 0.001051 0.025051 -0.021440 0.012478 0.160345 2.220148 Jarque-Bera Probability 1.066517 0.586690 Mean Median Maximum Minimum Std Dev Skewness Kurtosis -3.16e-15 0.000663 0.031153 -0.026961 0.016919 0.226024 2.095594 Jarque-Bera Probability 1.533448 0.464532 1 -0.02 -0.01 0.00 0.01 0.02 0.03 With dummies -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 Without dummies Figure 2: Histograms and Jarque-Bera tests Test for stationarity of residuals Table 8: Unit root test for residual series ADF With dummies Without dummies Critical value at significance level of 1% -7.7960 -7.8574 -2.6327 ADF statistics are negative and less than the critical value at significance level of 1%, p-value is 0.000 Therefore, the residual series are stationary and normal distributed, i.e they are white noise The coefficient of the first lag of Error Correction EC(-1) of the Error Correction Model (ECM) is -1.0859 (i.e negative and larger than -2), and statistically significant It confirms that it is dynamically stable (Loayza and Ranciere 2005) The results show that there is long-run relationship among the variables; the model has no autocorrelation, no heteroskedasticity, no multicollinearity; the residuals are white noise, the coefficients of the models are stable and model specifications are suitable Therefore, it can be said that regression models are appropriate and reliable RESULTS The ARDL model and its Conditional Error Correction model (ECM) can show the short-run and long-run relationship among variables In case the number of observations of the model is small, the ECM model gives more reliable results (Nkoro and Uko 2016) However, the coefficient of the ARDL model can be explained more easily and visually Table 9: Results of ARDL models and ECM model Variables ARDL models With dummies Coef Variables ECM models Without dummies SE Dependent variable Coef With dummies SE Coef CPUE Without dummies SE Dependent variable 0.6838 CPUE(-2) -0.4835 *** CPUE(-3) -0.2862 LnCapacity -0.5464 *** 0.1769 -0.5985 ** 0.2024 D(LnCapacity) -0.5464 *** 0.0744 -0.5985 *** 0.0902 LnCapacity(-1) 0.6154 *** 0.1984 0.5944 ** 0.2478 D(LnCapacity(-1)) 0.5763 *** 0.0893 0.6147 *** 0.1124 LnCapacity(-2) -0.3742 0.2523 -0.3427 0.2555 D(LnCapacity(-2)) 0.2021 * 0.0911 0.2720 ** 0.1125 LnCapacity(-3) -0.2021 0.2362 -0.2720 0.2619 LnLabour 0.3009 0.1744 0.2198 0.1572 D(LnLabour) 0.3009 ** 0.1112 0.2198 * 0.1163 LnLabour(-1) -0.0785 0.2071 -0.0220 0.2023 D(LnLabour(-1)) 0.0623 0.0937 -0.1866 LnLabour(-3) 0.1814 SST -0.0132 D(CPUE(-1)) 0.1643 -0.5697 ** 0.1975 0.2259 -0.2526 D(CPUE) CPUE(-1) -0.2437 0.1836 SE *** LnLabour(-2) 0.7368 *** Coef 0.2025 0.1935 0.4539 0.0384 -0.0111 0.2897 * 0.0326 SST(-1) -0.0936 0.0423 -0.1035 ** SST(-2) -0.1068 ** 0.0428 -0.1069 * SST(-3) -0.0690 ** 0.0257 -0.0530 0.0327 LnRainfall -0.0715 0.1490 -0.1241 0.1160 LnRainfall(-1) 0.0134 0.1304 0.0011 0.1398 -0.0285 LnRainfall(-3) 0.2873 Typhoon Typhoon(-1) 0.0383 0.0522 0.0916 0.0025 0.1106 0.2944 0.0010 0.0028 0.0028 0.0026 0.0017 0.0019 0.0021 0.0024 Water 2019, 11, x; doi: FOR PEER REVIEW ** 0.8223 *** 0.1142 D(CPUE(-2)) 0.2862 * 0.1097 0.2526 * 0.1272 0.0997 ** D(LnLabour(-2)) -0.1814 D(SST) -0.0132 * 0.0743 -0.4539 0.0170 -0.0111 0.1147 *** 0.0998 0.2348 * LnRainfall(-2) 0.0909 0.2131 -0.2674 0.1955 0.7697 *** D(SST(-1)) 0.1758 *** D(SST(-2)) 0.0690 ** D(LnRainfall) -0.0715 D(LnRainfall(-1)) -0.2588 *** D(LnRainfall(-2)) -0.2873 *** D(Typhoon) 0.0010 D(Typhoon(-1)) 0.0133 0.0199 0.1600 *** 0.0310 0.0221 0.0530 * 0.0231 0.0582 -0.1241 0.0616 0.0297 0.0704 -0.2969 *** 0.0807 0.0504 -0.2944 *** 0.0624 0.0015 0.0028 0.0023 0.0106 0.1064 www.mdpi.com/journal/water *** 0.0019 *** 0.0027 Typhoon(-2) -0.0062 Typhoon(-3) -0.0071 SOI SOI(-1) SOI(-2) 0.0035 -0.0049 0.0041 0.0031 -0.0057 0.0039 0.0171 0.0122 0.0085 0.0148 0.0302 0.0160 0.0248 0.0134 * 0.0231 SOI(-3) 0.0367 D1 -0.0073 D2 0.1726 C 11.768 0.0120 ** 0.0114 0.0182 0.0406 0.0111 ** 0.0071 *** 0.0017 0.0057 D(SOI) 0.0171 ** 0.0050 0.0085 D(SOI(-1)) -0.0598 *** 0.0074 -0.0367 *** *** D(SOI(-2)) 0.0051 EC(-1) -1.0859 0.0579 D1 -0.0073 ** 0.0509 D2 0.1726 *** 0.0257 * 5.2219 4.118 C 11.7682 *** 1.1504 10.443 0.0145 D(Typhoon(-2)) ** 0.1062 * 0.0021 0.0060 -0.0588 *** 0.0093 -0.0406 *** 0.0067 -1.0855 *** 0.1363 10.4426 *** 1.3094 0.0195 Adjusted R2 0.9903 0.9867 Adjusted R2 0.9121 0.8615 Prob (F-statistics) 0.0000 0.0000 Prob (F-statistics) 0.0000 0.0000 Durbin-Watson stat 2.5529 2.5626 2.5529 2.5626 Durbin-Watson stat n=36 *p