Abstract
Using a system dynamics model, we compute the realized environmental investment rate. Using this rate as the weight, the green welfare index is computed from the consumption index and environmental services index. The environmental investment rate that maximizes the present value of the future stream of green welfare indices is estimated and called the optimal environmental investment rate. The normalized gap between these two rates is called ‘green growth gap,’ which gauges the deviation of the economy from the dynamically optimal path. The Green Growth Index is also developed to express this concept in a more digestible manner. These concepts and index are expected to be useful in the performance assessment of the national green growth strategy. This chapter provides a system dynamics model that can serve as a basis for measurement and policy feedback for green growth as a national strategy of Korea.
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Notes
- 1.
See Chung (2008).
- 2.
The Four Major River restoration Project is a multi-purpose project being concurrently implemented in Han, Nakdong, Geum and Youngsan Rivers. This project was initiated as part of the Green New Deal Policy of the Korean government and its estimated total cost is 22.2 trillion won, approximately 17.3 billion USD. For more information see Han et al. (2011).
- 3.
- 4.
The dynamic policy index (GDPI) is combined with green welfare index (GWPI) to compute green growth index. More detail is explained in Sect. 3.4.
- 5.
Green welfare considers both GDP and environmental service together. GWI is the measurement for green welfare.
- 6.
For the complete equations of the model, see the Appendix.
- 7.
Environmental activity means the whole environment related activities, which includes environmental augmentation (Env_Augmentation_expenditure), environmental R&D (Environmental_R&D)and capital formation (Capital_formation).
- 8.
For more detail see Han et al. (2009).
- 9.
“Institutions” may include regulation, emission trading, carbon tax, or national GHG reduction target setting, etc.
References
Cho, K.-Y., & Na, I. (2004). GHG mitigation policies and technological innovation. Korea Economic Review, 51(3), 263–294 (in Korean).
Chung, R. K. (2008). Green growth: Opportunities for bioenergy development in Asia and the Pacific, ppt presentation file, UNESCAP.
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Gerlagh, R., & van der Zwaan, B. (2003). Gross world product and consumption in a global warming model with endogenous technological change. Resource and Energy Economics, 25, 35–57.
Goulder, L. H., & Schneider, S. H. (1999). Induced technological change and the attractiveness of CO2 abatement policies. Resource and Energy Economics, 21, 211–253.
Han, T.-W., Kim, G.-S., & Lim, D. (2009). Development of green growth index for the effective implementation of green growth strategy. GwaCheon: Ministry of Environment. (in Korean).
Han, T.-W., Lim, D., & Lee, C.-H. (2011). Green growth: Climate change (Green forum 2010, Vol. 3). Seoul: NRCS/Random House Korea.
UNEP. (2007). Overview of Republic of Korea’s National Strategy for green growth, prepared by the United Nations Environment Programme, as part of its Green Economy Initiative. Geneva: UNEP.
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Appendix: Stella Equations
Appendix: Stella Equations
accu_consmp(t) = accu_consmp(t - dt) + (consmp_flow) * dt
INIT accu_consmp = consmp_flow
INFLOWS:
consmp_flow = consumption/(discount_rate)^year
accu_env_svcs(t) = accu_env_svcs(t - dt) + (flow_env_svcs) * dt
INIT accu_env_svcs = { Place initial value here… }flow_env_svcs
INFLOWS:
flow_env_svcs = Env_Services/(discount_rate)^year
acc_GGI(t) = acc_GGI(t - dt) + (GGI_flow) * dt
INIT acc_GGI = GGI_flow
INFLOWS:
GGI_flow = GGI/(discount_rate)^year
Capital_Stock(t) = Capital_Stock(t - dt) + (capital_formation - depreciation) * dt
INIT Capital_Stock = 389230
INFLOWS:
capital_formation = investment*0.449+Env__Activity-Env_R&D-Env_Aug_Exp+cap_from_env_net_export
OUTFLOWS:
depreciation = Capital_Stock*.1
Environmental_Stock(t) = Environmental_Stock(t - dt) + (aug_flow - pollutionflow) * dt
INIT Environmental_Stock = 82.2426+aug_flow+pollutionflow
INFLOWS:
aug_flow = ((Env_Augmentation-delay(Env_Augmentation,1))^.91)*2.0
OUTFLOWS:
pollutionflow = ((-env_pollution_transform_fcn+delay_env_trans_fcn)^1.3)*0.7
Environmental_Technology_Stock(t) = Environmental_Technology_Stock(t - dt) + (Env_R&D) * dt
INIT Environmental_Technology_Stock = Env_R&D
INFLOWS:
Env_R&D = Env__Activity*R&D_intensity
sum_SE_E(t) = sum_SE_E(t - dt) + (Flow_SE_E) * dt
INIT sum_SE_E = Flow_SE_E
INFLOWS:
Flow_SE_E = SE_of_Env_Stock
sum_SE_K(t) = sum_SE_K(t - dt) + (SE_K_flow) * dt
INIT sum_SE_K = SE_K_flow
INFLOWS:
SE_K_flow = SE_of_Kapital
year(t) = year(t - dt) + (yearadd) * dt
INIT year = 1
INFLOWS:
yearadd = 1
year2(t) = year2(t - dt) + (year1) * dt
INIT year2 = year1+1989
INFLOWS:
year1 = initial_year
adj_factor = 40
cap_from_env_net_export = env_net_export_parameter*Env__Activity
consindex = 100+(consumption-300000)/100000*2 + 100
consumption = GDP-investment-Env__Activity
deg_of_fitness = sum_SE_E+sum_SE_K
delay_env_trans_fcn = DELAY(env_pollution_transform_fcn,1)
discount_rate = 1.02
Econ_Policy = 0.299
Env_Augmentation = logn(Env_Aug_Exp)*adj_factor
Env_Aug_Exp = Env__Activity*0.0037/Env_Policy
Env_Index = 100*(Env_Services-6.5)/4*2+100
env_net_export_parameter = 0.01
Env_Policy = (1-Econ_Policy)*env_weight*env_policy_factors
env_policy_factors = 1
env_pollution_transform_fcn = exp(8.816566621-0.916511998* logn(Pollution_Function))
Env_Services = 0.06*Environmental_Stock
env_weight = GWI_allocration_ratio*1
Env__Activity = GDP*Env_Policy
GDP = exp(-348.5008+0.3474*logn(0.875136597*Capital_Stock)+46.9874*logn(year2))
GGI = GGI_weight_2*Env_Index+(1-GGI_weight_2)*consindex
GGI_weight_2 = 0.3
GWI_allocration_ratio = 0.3
initial_year = 1
investment = GDP*Econ_Policy
Pollution_Function = 44.351+0.0001158*GDP-0.0000456*Environmental_ Technology_Stock
R&D_intensity = 0.05
SE_of_Env_Stock = ((Environmental_Stock-Data_Env_Stock)/Data_Env_Stock)^2
SE_of_GDP = GDP-Data_GDP
SE_of_Kapital = ((Capital_Stock-Data_Capital)/Data_Capital)^2
Data_Capital = GRAPH(TIME)
(0.00, 389231), (1.90, 504888), (3.80, 612933), (5.70, 707897), (7.60, 824348), (9.50, 979953), (11.4, 1.1e+006), (13.3, 1.3e+006), (15.2, 1.4e+006), (17.1, 1.5e+006), (19.0, 1.6e+006)
Data_Env_Stock = GRAPH(TIME)
(0.00, 82.5), (1.06, 84.3), (2.11, 86.2), (3.17, 88.1), (4.22, 90.9), (5.28, 93.1), (6.33, 95.5), (7.39, 101), (8.44, 104), (9.50, 106), (10.6, 106), (11.6, 106), (12.7, 107), (13.7, 109), (14.8, 107), (15.8, 111), (16.9, 110), (17.9, 112), (19.0, 111)
Data_GDP = GRAPH(TIME)
(0.00, 186691), (1.06, 226008), (2.11, 257525), (3.17, 290676), (4.22, 340208), (5.28, 398838), (6.33, 448596), (7.39, 491135), (8.44, 484103), (9.50, 529500), (10.6, 603236), (11.6, 651415), (12.7, 720539), (13.7, 767114), (14.8, 826893), (15.8, 865241), (16.9, 908744), (17.9, 975013), (19.0, 1e+006)
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Han, TW., Kim, GS., Lim, D. (2012). Green Growth Index and Policy Feedback. In: Vazquez-Brust, D., Sarkis, J. (eds) Green Growth: Managing the Transition to a Sustainable Economy. Greening of Industry Networks Studies, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4417-2_6
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DOI: https://doi.org/10.1007/978-94-007-4417-2_6
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