.

gis/output/all_cost.csv

@@ -1,33 +1,33 @@
 ,CWP,CWP,CWP
 ,coastal,lakes,streams
 1990,1585.4790617326894,1171.6587125641076,1590.1596819518659
-1991,1723.8799280535543,1263.3589635249764,1675.7044262607085
-1992,1978.0124293965002,1291.107742940616,1714.1894016757033
+1991,1723.8799280535543,1263.3589635249762,1675.7044262607085
+1992,1978.0124293965002,1291.1077429406157,1714.1894016757035
 1993,1593.8164564526242,1209.548303354735,1714.8854924086982
-1994,1916.440580349693,1366.4337969845221,1786.1906293156596
-1995,1863.552323116692,1288.6865388158078,1752.0035908331415
-1996,1808.4766821794678,1174.410019658537,1837.22105532365
+1994,1916.4405803496932,1366.4337969845221,1786.1906293156596
+1995,1863.552323116692,1288.6865388158078,1752.0035908331413
+1996,1808.4766821794678,1174.4100196585368,1837.22105532365
 1997,1879.426364638791,1372.0908353989469,1869.3969909580144
-1998,1859.7682780424298,1220.5737324449353,1927.0752848183602
-1999,1962.4950386140658,1252.1856639326154,1849.4815632789846
+1998,1859.7682780424298,1220.5737324449353,1927.07528481836
+1999,1962.495038614066,1252.1856639326152,1849.4815632789844
 2000,1783.8506587903382,1231.7858439867443,1746.0027737101261
-2001,1862.0387646044499,1282.8318640947987,1763.8352552177194
-2002,1886.2706275545083,1546.6875577340506,1686.7840957986177
+2001,1862.03876460445,1282.8318640947987,1763.8352552177194
+2002,1886.2706275545083,1546.6875577340506,1686.7840957986175
 2003,1810.936277288613,1289.2991420666601,1625.178689465942
-2004,2013.6934900349827,1194.2334899168545,1787.7113233134908
+2004,2013.6934900349827,1194.2334899168545,1787.7113233134905
 2005,1400.2044468620293,1194.063299865544,1704.6002319779063
-2006,2118.037717321533,1353.336882217365,1719.7423059449409
-2007,2032.9688475071653,1346.5914546270803,1825.47320385538
+2006,2118.037717321533,1353.3368822173647,1719.7423059449409
+2007,2032.9688475071655,1346.5914546270803,1825.47320385538
 2008,2079.9768008666188,1245.9240020879095,1722.949746914103
-2009,1813.7065536688003,1102.7136259270833,1714.4942209909716
+2009,1813.7065536688003,1102.713625927083,1714.4942209909718
 2010,1976.931491959753,1164.5619270556883,1799.9368542825057
-2011,2121.666466097306,1221.8188907353483,1787.7664886900918
+2011,2121.6664660973056,1221.8188907353483,1787.7664886900916
 2012,1128.4490599401877,1201.9747671850785,1711.7103578591739
-2013,1935.9584103221555,1405.8349139292905,1795.8465759453193
+2013,1935.9584103221555,1405.8349139292902,1795.8465759453193
 2014,1845.9377166158197,1372.682300204288,1724.6525330845338
 2015,1860.7063417058396,1296.1957994655386,1804.9376791393904
 2016,2631.1037552129196,1301.4405172786276,1802.5335462668127
-2017,1503.3358406405255,1435.8460750669374,1894.8593848058179
+2017,1503.3358406405257,1435.8460750669374,1894.8593848058179
 2018,1873.0431001205386,1518.039925645777,1868.5100616252348
 2019,1803.9661839854111,1506.8395499940978,1812.0705888600396
 2020,2671.828142328489,1320.2231711241698,1698.2969399176534

gis/output/all_investment.csv

@@ -1,33 +1,33 @@
 ,IV,IV,IV
 ,coastal,lakes,streams
-1990,26310.64522280364,-10610.95641914526,21913.80959439866
-1991,-26144.64299724968,-55651.27856321739,-67817.06019839067
-1992,-114373.38098611057,-8304.90437584965,-37798.28928571609
+1990,26310.645222803636,-10610.956419145261,21913.80959439866
+1991,-26144.642997249663,-55651.27856321739,-67817.06019839067
+1992,-114373.3809861106,-8304.904375849648,-37798.289285716084
 1993,194511.77010944864,77388.12142340938,23360.281465004562
-1994,-115453.83733485537,-88136.61394890914,12644.642672975804
-1995,65309.419200226046,99231.69764573332,87419.40560217023
+1994,-115453.83733485537,-88136.61394890913,12644.642672975802
+1995,65309.41920022605,99231.69764573332,87419.40560217023
 1996,38958.632866855536,103008.36785813524,-79294.55647148077
-1997,-24126.13965912284,-130112.72188089184,16463.026525881603
-1998,39158.944842883204,141178.37309529688,-13527.606377201195
-1999,-60707.42739512611,-19544.338171440802,104721.92227660079
-2000,128130.38650657418,49349.23682858038,122666.77769343492
-2001,-19628.557657765785,-34688.327391496874,1901.5514542127096
-2002,13039.111159942466,-175126.56898396264,85671.91033540899
-2003,34319.70651882137,173347.55406450923,56661.917261838615
-2004,-53635.84275735006,87950.11488661008,-105130.37870422746
-2005,224497.80782858384,16256.900648492112,108671.1266929596
-2006,-183475.6933836786,-88708.4527554455,52212.788040466265
+1997,-24126.13965912284,-130112.72188089184,16463.026525881607
+1998,39158.9448428832,141178.37309529688,-13527.606377201198
+1999,-60707.42739512611,-19544.338171440806,104721.92227660077
+2000,128130.38650657418,49349.23682858039,122666.77769343492
+2001,-19628.557657765785,-34688.32739149688,1901.5514542127041
+2002,13039.111159942464,-175126.56898396264,85671.91033540899
+2003,34319.70651882136,173347.55406450923,56661.917261838615
+2004,-53635.84275735004,87950.11488661007,-105130.37870422746
+2005,224497.8078285838,16256.900648492112,108671.12669295962
+2006,-183475.6933836786,-88708.45275544551,52212.78804046627
 2007,71347.13049562045,44433.04922651515,-46585.27214284922
-2008,-13331.086426978547,65319.175944796196,48750.03257998035
+2008,-13331.086426978543,65319.175944796196,48750.03257998033
 2009,126350.69691396243,58805.31836144889,-41678.1475867677
-2010,-46164.29721003682,-34965.75965799915,-15449.022162872581
-2011,-35036.286837174885,-49594.480337995265,-8736.59381771393
-2012,340287.85251409974,7989.866128003807,85125.236209073
-2013,-185386.94026506963,-126843.28794477873,-67689.93664239775
-2014,46265.198875165916,39128.06567961996,98643.36545886096
-2015,29028.293963700624,127778.3538634372,19791.383008741435
-2016,-285443.928912038,-12042.128080703871,45550.52170133617
+2010,-46164.29721003682,-34965.75965799916,-15449.022162872585
+2011,-35036.286837174855,-49594.48033799526,-8736.593817713932
+2012,340287.85251409974,7989.8661280038095,85125.236209073
+2013,-185386.94026506963,-126843.28794477871,-67689.93664239775
+2014,46265.198875165916,39128.06567961996,98643.36545886095
+2015,29028.293963700628,127778.3538634372,19791.383008741435
+2016,-285443.928912038,-12042.128080703867,45550.52170133617
 2017,399262.36745669344,-66410.49186548415,-35410.29098460917
-2018,-116217.11662294526,-55238.150439291545,62732.39566281368
-2019,40235.0736869619,47765.93570986419,45745.08692606474
-2020,-216914.21967857392,134125.2759081712,120754.64760616298
+2018,-116217.11662294526,-55238.150439291545,62732.39566281367
+2019,40235.073686961914,47765.935709864185,45745.08692606474
+2020,-216914.2196785739,134125.2759081712,120754.64760616298

gis/script.py

@@ -185,9 +185,9 @@
 df_BT.to_csv("output\\all_eco_imp.csv")  #  save to csv
 
 # Marginal willingness to pay (MWTP) for improvement of water quality to "Good"
-CWPn = c.valuation(df_BT, real=False)
+CWPn_j = c.valuation(df_BT, real=False)
 # Nominal cost of pollution in prices of current year, and preceding year respectively
-CWPn.columns = CWPn.columns.set_levels(
+CWPn_j.columns = CWPn_j.columns.set_levels(
     [
         "Cost (current year's prices, million DKK)",
         "Cost (preceding year's prices, million DKK)",
@@ -196,9 +196,9 @@
 )
 
 # Investment in water quality (net present value of infinite stream of MWTP for change)
-IVn = c.valuation(df_BT, real=False, investment=True)
+IVn_j = c.valuation(df_BT, real=False, investment=True)
 # Nominal investment value in prices of current year and preceding year respectively
-IVn.columns = IVn.columns.set_levels(
+IVn_j.columns = IVn_j.columns.set_levels(
     [
         "Investment value (current year's prices, million DKK)",
         "Investment value (preceding year's prices, million DKK)",
@@ -207,31 +207,33 @@
 )
 
 # Merge cost of pollution and investment value of increase (decrease) in water quality
-nominal = pd.concat([CWPn, IVn], axis=1)
+nominal = pd.concat([CWPn_j, IVn_j], axis=1)
 nominal.to_excel("output\\all_nominal.xlsx")  # manually Wrap Text row 1 & delete row 3
 
 
 ########################################################################################
-#   4.c Real cost of pollution and investment in water quality for national accounts
+#   4.c Real cost of water pollution and investment in water quality for journal article
 ########################################################################################
-# Costs of pollution in real terms (million DKK, 2018 prices)
+# Costs of Water Pollution (CWP) in real terms (million DKK, 2018 prices)
 CWP_v = c.valuation(df_BT)
-CWP = CWP_v.groupby(["j", "t"]).sum().unstack(level=0).rename_axis(None)  #  sum over v
-CWP.rename_axis([None, None], axis=1).to_csv("output\\all_cost.csv")
+CWP_j = (
+    CWP_v.groupby(["j", "t"]).sum().unstack(level=0).rename_axis(None)
+)  #  sum over v
+CWP_j.rename_axis([None, None], axis=1).to_csv("output\\all_cost.csv")
 f2 = (
-    CWP.loc[:, "CWP"]
+    CWP_j.loc[:, "CWP"]
     .rename_axis(None, axis=1)
     .plot(ylabel="Cost of current water pollution (million DKK, 2018 prices)")
     .get_figure()
 )
 f2.savefig("output\\all_cost.pdf", bbox_inches="tight")
 
-# Investment value in real terms (million DKK, 2018 prices)
-IV_v = c.valuation(df_BT, investment=True)
-IV = IV_v.groupby(["j", "t"]).sum().unstack(level=0).rename_axis(None)  #  sum over v
-IV.rename_axis([None, None], axis=1).to_csv("output\\all_investment.csv")
+# Investment Value of water quality improvement in real terms (million DKK, 2018 prices)
+IV_vj = c.valuation(df_BT, investment=True)
+IV_j = IV_v.groupby(["j", "t"]).sum().unstack(level=0).rename_axis(None)  #  sum over v
+IV_j.rename_axis([None, None], axis=1).to_csv("output\\all_investment.csv")
 f2 = (
-    IV.loc[:, "IV"]
+    IV_j.loc[:, "IV"]
     .rename_axis(None, axis=1)
     .plot(
         kind="bar",
@@ -241,6 +243,8 @@
 )
 f2.savefig("output\\all_investment.pdf", bbox_inches="tight")
 
+IV = IV_j.sum(axis=1)  #  sum over j
+
 
 ########################################################################################
 #   5. Decompose development by holding everything else equal

index.md

@@ -6,9 +6,10 @@ The second part consists of imputing missing values in the dataset. The imputed
 The third part applies valuation studies to be able to estimate the development in the Green Net National Income (GNNI) for Denmark. For surface water, we apply the Benefit Transfer equation estimated by a meta analysis function ([Zandersen et al, 2022 - *English summary*](https://dce.au.dk/udgivelser/vr/nr-451-500/abstracts/no-486-socio-economic-benefits-of-improved-water-quality-development-and-use-of-meta-analysis-function-for-benefit-transfer)) to help answer the overall research question, to what extent economic growth can be considered sustainable or at the cost of the environment?
 
 ## Imputed ecological status
+Our dataset indicates an overall improvement of the water environment since 1990. However, since agricultural Nitrogen surplus peaked in the mid 1980s, we are most likely only observing the latter half of an inverse U-shape of water pollution ([Hansen et al, 2011](https://doi.org/10.1021/es102334u)).
 
 #### Less than good ecological status (share of shore length)
-![Ecological status](https://github.com/thornoe/GreenGDP/blob/master/gis/output/all_eco_imp_LessThanGood.png)
+![Ecological status](https://github.com/thornoe/GreenGDP/blob/master/gis/output/all_eco_imp_LessThanGood.png?raw=true)
 
 ## Identification of waterbodies
 This project follows the selection and demarcation given by **VP2** *([Vandområdeplan 2 - Danish](https://mst.dk/natur-vand/vandmiljoe/vandomraadeplaner/vandomraadeplaner-2015-2021/))* which is the Danish waterbody plan for 2015-2021 that implements phase two of the **EU-WFD** ([the European Union's Water Framework Directive - *English*](https://ec.europa.eu/environment/water/water-framework/)). The surface waterbodies in VP2 are constituted by a total of 6979 streams, 857 lakes and 119 coastal waters.