Identifying Countries for Regional Cooperation in Low Carbon Growth: A Geo-environmental Impact Index

Abstract

This paper proposes a new geo-environmental Impact index to quantify the implications and dynamics for a country to join in a regional cooperation for low carbon growth (LCG) in Asia. The index helps differentiating the countries according to risk dissemination and risk assimilation categories, which are so crucial in framing effective LCG policies. Empirical results reveal that under the proposed grand regional bloc comprising of 20 Asian countries, eight countries are identified as predominantly geo-environmental risk assimilators, one risk neutral, while the rest of the countries are identified as predominantly risk disseminators. Empirical results also show that synergy effect is evident in all the regional or sub-regional groupings. Sensitivity analysis indicates that the proposed grand regional bloc would yield higher possibility for reducing CO₂ emissions in the respective countries as compared to the actions taken by separate sub-regional groupings. The proposed model can also be used as an imperative tool in resolving the regional disputes under the climate change negotiations.

Highlights

• A new Geo-Environmental Importance (GEI) index is estimated to quantify the impact of regional cooperation (RC) for low carbon green growth (LCG).

  Using the GEI the following research questions are answered:

  • How much would it benefit a country if its partner countries could reduce the carbonization activities (i.e., emission and environmental degradation) to a certain level? and vice versa.

  • What should be the costs and payoffs of the countries’ decisions under a game-theoretic approach?

  • Under a regional cooperation framework, how the roles and liabilities of each country can be quantified?

  • How effective the RC bloc would be in achieving overall CO₂ reduction in the region.

Appendix

Proposition 2

Country with proximity should matter in RC for LCG.

Let’s consider country-j as the reference country.

GEI of country-i on country-j:

$${\text{GEI}}_{i,j} = \frac{{A_{j} }}{{A_{i} }} \times \frac{{m_{i} }}{{m_{j} }} \times \frac{{g(m_{i} )}}{{g(m_{j} )}} \times \theta_{i,j} .$$

GEI of country-k on country-j:

$${\text{GEI}}_{k,j} = \frac{{A_{j} }}{{A_{k} }} \times \frac{{m_{k} }}{{m_{j} }} \times \frac{{g(m_{k} )}}{{g(m_{j} )}} \times \theta_{k,j} .$$

$${\text{Therefore}},\,\frac{{{\text{GEI}}_{k,j} }}{{{\text{GEI}}_{i,j} }} = \frac{{A_{i} }}{{A_{k} }} \times \frac{{m_{k} }}{{m_{i} }} \times \frac{{g(m_{k} )}}{{g(m_{i} )}} \times \frac{{\theta_{k,j} }}{{\theta_{i,j} }}.$$

(17)

Let’s consider the following cases as examples.

Case 1

Country-i and country-k with the same CO₂ emission and , but different area.

Conditions are: $m_{i}$ = $m_{k}$ and $\theta_{i,j}$ = $\theta_{k,j}$.

Therefore, from Eq. (17) the following Eq. (18) can be derived:

$$\frac{{{\text{GEI}}_{k,j} }}{{{\text{GEI}}_{i,j} }} = \frac{{A_{i} }}{{A_{k} }} \times \frac{{g(m_{k} )}}{{g(m_{i} )}}.$$

(18)

Practically, the range of growth factor $g(m_{i} )$ or $g(m_{k} )$ swivels between 0.9 and 1.1; i.e., from 10% reduction to 10% increase in growth rate, on average. This would imply the probable range of $\frac{{g(m_{k} )}}{{g(m_{i} )}}$ as $\left( {\frac{1 - 0.1}{1 + 0.1}} \right)$ to $\left( {\frac{1 + 0.1}{1 - 0.1}} \right)\,{\text{i}} . {\text{e}}.$ 0.82–1.22.

For simplification, it is assumed that $\frac{{g(m_{k} )}}{{g(m_{i} )}} \approx 1$.

Hence, Eq. (18) implies,

$$\frac{{{\text{GEI}}_{k,j} }}{{{\text{GEI}}_{i,j} }} = \frac{{A_{i} }}{{A_{k} }}.$$

(19)

Again, $\theta_{i,j} = \frac{{{\text{arc}}_{i} }}{{{\text{dist}}_{i,j} }},$

$$\theta_{k,j} = \frac{{{\text{arc}}_{k} }}{{{\text{dist}}_{k,j} }}.$$

Hence, considering $\theta_{i,j}$ = $\theta_{k,j}$,

$$\frac{{{\text{arc}}_{i} }}{{{\text{dist}}_{i,j} }} = \frac{{{\text{arc}}_{k} }}{{{\text{dist}}_{k,j} }}.$$

Since ${\text{dist}}_{i,j} < {\text{dist}}_{k,j}$ is assumed, therefore,

$${\text{arc}}_{i} < {\text{arc}}_{k} .$$

Typically this implies that the area of country-k is bigger than country-i.

Now, if the area of country-k is larger than country-i, from Eq. (19)

$${\text{GEI}}_{i,j} > {\text{GEI}}_{k,j} .$$

So, the closer the country, ceteris paribus, more impact it has than the far-distant country.

Case 2

Country-i and country-k with the same area and CO₂ emission but different $\theta$.

Conditions of $A_{i} = A_{k}$, $m_{i}$ = $m_{k}$ and $\frac{{g(m_{k} )}}{{g(m_{i} )}} \approx 1$ will take the equation as:

$$\frac{{{\text{GEI}}_{k,j} }}{{{\text{GEI}}_{i,j} }} = \frac{{\theta_{k,j} }}{{\theta_{i,j} }}.$$

(20)

Again,

$$\theta_{i,j} = \frac{{{\text{arc}}_{i} }}{{{\text{dist}}_{i,j} }},$$

$$\theta_{k,j} = \frac{{{\text{arc}}_{k} }}{{{\text{dist}}_{k,j} }}.$$

For similar area, we can assume that, arc_i = arc_k.

So,

$$\theta_{k,j} = \frac{{{\text{dist}}_{i,j} }}{{{\text{dist}}_{k,j} }} \times \theta_{i,j} .$$

Therefore, if country-k is assumed far from country-i, then

$${\text{dist}}_{k,j} > {\text{dist}}_{i,j} .$$

If $\theta_{k,j} < \theta_{i,j}$, then from Eq. (20):

$${\text{GEI}}_{i,j} > {\text{GEI}}_{k,j} .$$

So, the closer the country, ceteris paribus, is of more impact than the far-distant countries.

Case 3

Country-i and country-k with the same area and $\theta$, but with different CO₂ emissions.

Conditions of $A_{i} = A_{k}$,$\theta_{i,j}$ = $\theta_{k,j}$ and $\frac{{g(m_{k} )}}{{g(m_{i} )}} \approx 1$ will take the Eq. (17) as:

$$\frac{{{\text{GEI}}_{k,j} }}{{{\text{GEI}}_{i,j} }} = \frac{{m_{k} }}{{m_{i} }}.$$

Therefore, with such equal proximity, higher the level of CO₂ emission, the greater impact of that country will be for the reference country.