Security Challenges and Air Quality Management in India: Emissions Inventory and Forecasting Estimates
Abstract
:1. Introduction
- (i)
- Literature on the Keynesian ‘defense burden (KDB) hypothesis’;
- (ii)
- Reversal of the Keynesian hypothesis called spillover effect;
- (iii)
- Country-wide and regional assessment of the stated hypotheses; and
- (iv)
- Rise and fall in a nation’s output because of increasing military activity.
- (i)
- To examine the impact of arms imports on carbon emissions, following the ‘emissions-defense burden hypothesis’.
- (ii)
- To analyze the role of military expenditures and armed forces personnel on carbon emissions, following the ‘cleaner-emissions hypothesis’ in a country, and
- (iii)
- To investigate the nation’s economic activities with regard to the arms transfers towards environmental protection agenda.
2. Materials and Methods
2.1. Theoretical Underpinning
- (i)
- Emissions-defense postulate (Treadmill of destruction view)
- (ii)
- Emissions-cleaner postulate (Treadmill of production view), and
- (iii)
- Extended version of the non-linear hypothesis, called asymmetric-emissions postulate
- (i)
- Emissions-Defense Postulate (Treadmill of destruction view): The emissions-defense postulate shows the crowding out a situation where government allocate a greater sum of money on military activities instead of consumer goods, including education and healthcare expenditures [40,41,42]. It is evident that military activities adversely affect the natural environment and precious natural resource capital, damaging human health and the natural environment with low spending on improving socio-economic infrastructure [21,43,44]. The greater supply of arms ammunition threatens regional security. It increased the risk of arms conflicts, which enforce increased military spending that adversely affects its affluence and natural resource capital [45].
- (ii)
- Emissions-Cleaner Postulate (Treadmill of production view): The supply-side spillovers are associated with the of lowering military expenditures that increase spending on consumer goods to increase the country’s aggregate demand [46,47,48]. The emissions-cleaner postulate is designed in line with the stated spillovers effect. Lowering military expenditures positively impacts air quality indicators and improves the eco-system, increasing the nation’s aggregate demand for eco-friendly goods. Hence, the viability of preventing ecological damage and improving air quality levels can be attained by investing in military equipment and weapons that are designed in a way to reduce lead-free ammunition supply in a country, and
- (iii)
- Asymmetric-Emissions Postulate: The non-linear relationship between military factors and economic growth is earlier accessed through doubling the military items to see the rise and fall in growth-specific factors in the earlier literature [19,38]. The study assessed the non-linear relationships asymmetrically to observe the positive and negative variations in the military factors on carbon emissions in a country to verify asymmetric-emissions postulate. The asymmetric-emission postulate can confirm either the ‘emissions burden hypothesis’ or ‘emissions cleaner hypothesis’ through absorbing positive and negative shocks about the specified military factors during the stated period.
2.2. Econometric Framework
- (i)
- NARDL estimator is equally applicable for the level variables, i.e., I(0) series, as ARDL estimator.
- (ii)
- NARDL estimator gives decent inferences for the first differenced variables, i.e., I(1) series, as ARDL estimator.
- (iii)
- NARDL is equally viable for I(0) and I(1) variables as ARDL estimator.
- (iv)
- The error correction term can easily be computed in NARDL as an ARDL estimator.
- (v)
- The NARDL is equally applicable for the finite sample data set as an ARDL estimator.
- (vi)
- The same lag length criterion can be used in NARDL as an ARDL estimator.
- (vii)
- The imposition of restrictions on short-term and long-term variables through Wald F-statistics can easily be applicable in NARDL as an ARDL estimator.
- (viii)
- The long-run cointegrated relationship between the stated variables is equally validated in the NARDL system as an ARDL estimator.
- (ix)
- The procedure of applying diagnostic testing for evaluating normality test, autocorrelation, heteroskedasticity, and Ramsey RESET test is the same in both the estimators.
- (x)
- CUSUM and CUSUM square test for model stability is performed in both the test with a similar procedure.
- (i)
- The ARDL estimator identifies the short- and long-run coefficients in linear terms, while NARDL coefficients are estimated in non-linear terms.
- (ii)
- The variables are decomposed into positive and negative series to estimate asymmetric plots in the NARDL estimator while not being exercised in the ARDL estimator.
- (iii)
- The NARDL specifications can be used in different cointegration processes, like Fully Modified OLS, Dynamic OLS, robust least squares estimator, etc., which gives asymmetric estimates. In contrast, the ARDL estimator cannot perform similarly to obtain dynamic inferences.
- (iv)
- The asymmetric Granger causality estimates allow more insights to be made about causal inferences compared to the ARDL estimator.
- (v)
- The innovation accounting matrix for evaluating forecast coefficient estimates can be used by positive and negative shocks of the candidate variables compared to the ARDL estimator over a time horizon.
3. Results
- (i)
- The ’ammunition-emissions hypothesis’ is supported by using arms imports and arms forces personnel concerning carbon emissions [59].
- (ii)
- Unsustainable production and consumption are leading to increasing carbon emissions in a country [60].
- (iii)
- Emissions-defense burden and crowding out situation is verified with important military factors and carbon emissions [61].
- (iv)
- The ‘cleaner-emissions hypothesis’ is substantiated that increases a country’s aggregate demand by lowering carbon emissions through eco-friendly arms transfers [62].
4. Discussion
- (i)
- Arms imports and military expenditures will follow the ‘treadmill theory of destruction’.
- (ii)
- The ‘cleaner-emissions hypothesis’ is likely to become visible with negative shocks of armed forces personnel supporting the spillover hypothesis.
- (iii)
- The country’s affluence is likely to support cleaner emissions agenda in the wake of arms transfers in a country.
5. Conclusions and Policy Implications
- (i)
- Excessive arms transfers confirm the ‘emissions-defense burden hypothesis’, which increases carbon emissions while deteriorating the country’s green development agenda, which must be reduced by managing ammunition safety. The supply of lead-containing ammunition generates complex gases and particles, including carbon emissions, raising risks to human health. Lead-free ammunition reduces carbon content in the atmosphere, which aids in achieving the healthcare sustainability agenda. Aircraft, bulletproof vehicles, weaponry, radar systems, and military-used ships are among the items transferred. The following arms transfers should be environmentally friendly:
- (a)
- The use of advanced cleaner technologies aids in the greening of aviation manufacturing.
- (b)
- Arms transfer treaties aid in the reduction in illicit arms flows, thereby promoting the United Nations Sustainable Development Goal 16.
- (c)
- To avoid negative environmental externalities, cleaner fuels should be used in armored vehicles and military ships.
- (ii)
- Armed forces personnel and military spending confirm the ‘cleaner emissions hypothesis’, implying that the army in the field is equipped with green technology armaments to reduce carbon emissions through armament engineering. Furthermore, the defense burden is significantly reduces in order to move forward with the clean agenda. In the long run, the spillover effect helps increase aggregate demand for environmentally friendly goods due to increasing international pressure to conserve ecological resources. The country should reduce armed tensions to make progress toward the environmental sustainability agenda in the region.
- (iii)
- The country’s economic growth raises carbon emissions while increasing armament imports. Significant economic and environmental reforms are required for the country’s consumption and production processes to be green and clean. A few corrective actions are suggested, such as,
- (a)
- Armed conflicts should be resolved through dialogue and the peace movement, contributing to global prosperity.
- (b)
- Arms regulations should be implemented in a region to reduce illicit arms flows.
- (c)
- Armaments should be supplied following global environmental standards.
- (d)
- Using renewable fuels instead of nonrenewable fuels in aviation, armored vehicles, and aircraft ships help to reduce carbon emissions.
- (e)
- Significant reductions in military spending and arms transfers are likely to increase spending on education and healthcare infrastructure, which is considered one of the vital aspects of the United Nations sustainable development goals.
- (f)
- Strict ecological reforms in the nations’ consumption and goods production are critical to healthier development.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- United Nations. Goal 16: Peace, Justice and Strong Institutions. 2015. Available online: https://sdgs.un.org/goals/goal16 (accessed on 13 September 2021).
- Anser, M.K.; Abbas, S.; Nassani, A.A.; Haffar, M.; Zaman, K.; Abro, M.M.Q. Innovative Carbon Mitigation Techniques to Achieve Environmental Sustainability Agenda: Evidence from a Panel of 21 Selected R&D Economies. Atmosphere 2021, 12, 1514. [Google Scholar] [CrossRef]
- Fang, C.; Gao, H.; Li, Z.; Wang, J. Regional Air Pollutant Characteristics and Health Risk Assessment of Large Cities in Northeast China. Atmosphere 2021, 12, 1519. [Google Scholar] [CrossRef]
- Yoda, Y.; Tamura, K.; Otani, N.; Hasunuma, H.; Nakayama, S.F.; Shima, M. Reduction in Indoor Airborne Endotoxin Concentration by the Use of Air Purifier and Its Relationship with Respiratory Health: A Randomized Crossover Intervention Study. Atmosphere 2021, 12, 1523. [Google Scholar] [CrossRef]
- Aryan, J. The Evolving Landscape of India’s Arms Trade. 2021. Available online: https://www.orfonline.org/expert-speak/the-evolving-landscape-of-indias-arms-trade/ (accessed on 13 September 2021).
- World Bank. World Development Indicators 2021; World Bank: Washington, DC, USA, 2021. [Google Scholar]
- Alptekin, A.; Levine, P. Military expenditure and economic growth: A meta-analysis. Eur. J. Politial Econ. 2012, 28, 636–650. [Google Scholar] [CrossRef] [Green Version]
- Luqman, M.; Antonakakis, N. Guns better than butter in Pakistan? The dilemma of military expenditure, human development, and economic growth. Technol. Forecast. Soc. Chang. 2021, 173, 121143. [Google Scholar] [CrossRef]
- Çolak, O.; Özkaya, M.H. The Nexus between External Debts and Military Expenditures for the Selected Transition Economies: A Panel Threshold Regression Approach. Def. Peace Econ. 2020, 1–17. [Google Scholar] [CrossRef]
- Syed, A. The asymmetric relationship between military expenditure, economic growth and industrial productivity: An empirical analysis of India, China and Pakistan via the NARDL approach. Rev. Finanz. Polít. Econ. 2021, 13, 77–97. [Google Scholar] [CrossRef]
- Azam, M. Does military spending stifle economic growth? The empirical evidence from non-OECD countries. Heliyon 2020, 6, e05853. [Google Scholar] [CrossRef] [PubMed]
- Khalid, M.A.; Razaq, M.A.J.A. The Relationship between Military Expenditure and Economic Growth in Middle East and North Africa (Mena) Countries. J. Def. Resour. Manag. 2021, 12, 99–116. [Google Scholar]
- Dimitraki, O.; Win, S. Military Expenditure Economic Growth Nexus in Jordan: An Application of ARDL Bound Test Analysis in the Presence of Breaks. Def. Peace Econ. 2020, 1–18. [Google Scholar] [CrossRef]
- Meidutė-Kavaliauskienė, I.; Dudzevičiūtė, G.; Maknickienė, N. Military and demographic inter-linkages in the context of the Lithuanian sustainability. J. Bus. Econ. Manag. 2020, 21, 1508–1524. [Google Scholar] [CrossRef]
- Ullah, A.; Zhao, X.; Kamal, M.A.; Zheng, J. Modeling the relationship between military spending and stock market development (a) symmetrically in China: An empirical analysis via the NARDL approach. Phys. A Stat. Mech. Appl. 2020, 554, 124106. [Google Scholar] [CrossRef]
- Saba, C.S.; Ngepah, N. Convergence in military expenditure and economic growth in Africa and its regional economic communities: Evidence from a club clustering algorithm. Cogent Econ. Financ. 2020, 8, 1832344. [Google Scholar] [CrossRef]
- Sahu, A.K. Heterogeneous security complex: A framework for the analysis of the China-India water conflict and South Asia. In Re-Imagining Border Studies in South Asia; Routledge: New Delhi, India, 2020; pp. 193–216. [Google Scholar]
- Mastro, O.; Tarapore, A. Asymmetric but uneven: The China-India conventional military balance. In Routledge Handbook of China-India Relations; Tarapore, A., Bajpai, K., Ho, S., Miller, M.C., Eds.; Routledge: London, UK, 2020; pp. 235–247. [Google Scholar]
- Zaman, K.; Khan, H.U.R.; Islam, T.; Yousaf, S.U.; Nassani, A.A.; Khan, A.; Mustaffa, M.S.; Ahmad, J.; Hishan, S.S.; Aamir, A. Does higher military spending affect business regulatory and growth specific measures? Evidence from the group of seven (G-7) countries. Econ. Polit. 2019, 36, 323–348. [Google Scholar] [CrossRef]
- Ul Ain, Q.; Rais, S.I.; Shah ST, H.; Zaman, K.; Ejaz, S.; Mansoor, A. Empirically testing Keynesian defense burden hypothesis, nonlinear hypothesis, and spillover hypothesis: Evidence from Asian countries. Theor. Appl. Econ. 2019, 26, 169–182. [Google Scholar]
- Qayyum, U.; Anjum, S.; Sabir, S. Armed conflict, militarization and ecological footprint: Empirical evidence from South Asia. J. Clean. Prod. 2021, 281, 125299. [Google Scholar] [CrossRef]
- Ahmed, Z.; Zafar, M.W.; Mansoor, S. Analyzing the linkage between military spending, economic growth, and ecological footprint in Pakistan: Evidence from cointegration and bootstrap causality. Environ. Sci. Pollut. Res. 2020, 27, 41551–41567. [Google Scholar] [CrossRef]
- Ali, H.E. Natural Resource Rents and Military Expenditures in the Middle East and North Africa: A Long-run Perspective. Research Handbook on the Arms Trade. Edward Elgar Publishing, 2020. Available online: https://www.elgaronline.com/view/edcoll/9781789900989/9781789900989.00016.xml/ (accessed on 13 September 2021).
- Sohag, K.; Taşkın, F.D.; Malik, M.N. Green economic growth, cleaner energy and militarization: Evidence from Turkey. Resour. Policy 2019, 63, 101407. [Google Scholar] [CrossRef]
- Wang, K.H.; Su, C.W.; Lobonţ, O.R.; Umar, M. Whether crude oil dependence and CO2 emissions influence military expenditure in net oil importing countries? Energ. Policy 2021, 153, 112281. [Google Scholar] [CrossRef]
- Meulewaeter, C.; Brunet, P. Military spending and climate change. In Military Spending and Global Security; Routledge: London, UK, 2020; pp. 103–117. Available online: https://www.taylorfrancis.com/chapters/edit/10.4324/9781003045823-8/military-spending-climate-change-chlo%C3%A9-meulewaeter-pere-brunet (accessed on 14 September 2021).
- Pathak, S. Ecological footprints of war: An exploratory assessment of the long-term impact of violent conflicts on national biocapacity from 1962–2009. J. Environ. Stud. Sci. 2020, 10, 380–393. [Google Scholar] [CrossRef]
- Wang, K.-H.; Su, C.-W. Does high crude oil dependence influence Chinese military expenditure decision-making? Energy Strat. Rev. 2021, 35, 100653. [Google Scholar] [CrossRef]
- Ullah, S.; Ozturk, I.; Majeed, M.T.; Ahmad, W. Do technological innovations have symmetric or asymmetric effects on environmental quality? Evidence from Pakistan. J Clean Prod. 2021, 316, 128239. [Google Scholar] [CrossRef]
- Alola, A.A.; Ozturk, I.; Bekun, F.V. Is clean energy prosperity and technological innovation rapidly mitigating sustainable energy-development deficit in selected sub-Saharan Africa? A myth or reality. Energy Policy 2021, 158, 112520. [Google Scholar] [CrossRef]
- Abbasi, K.R.; Hussain, K.; Redulescu, M.; Ozturk, I. Does natural resources depletion and economic growth achieve the car-bon neutrality target of the UK? A way forward towards sustainable development. Resour. Policy 2021, 74, 102341. [Google Scholar] [CrossRef]
- Khan, M.; Ozturk, I. Examining the direct and indirect effects of financial development on CO2 emissions for 88 developing countries. J. Environ. Manag. 2021, 293, 112812. [Google Scholar] [CrossRef] [PubMed]
- Gómez-Trueba Santamaría, P.; Arahuetes García, A.; Curto González, T. A tale of five stories: Defence spending and economic growth in NATO´ s countries. PLoS ONE 2021, 16, e0245260. [Google Scholar]
- Nadeem, M.A.; Liu, Z.; Xu, Y.; Nawaz, K.; Malik, M.Y.; Younis, A. Impacts of terrorism, governance structure, military expenditures and infrastructures upon tourism: Empirical evidence from an emerging economy. Eurasian Bus. Rev. 2020, 10, 185–206. [Google Scholar] [CrossRef]
- Sarwar, S.; Idrees, A.S. Impact of Military Expenditures on the Globalization Process: A Spatial Econometric Analysis for African Region. J. Asian Afr. Stud. 2021. [Google Scholar] [CrossRef]
- Maher, M.; Zhao, Y. Do Political Instability and Military Expenditure Undermine Economic Growth in Egypt? Evidence from the ARDL Approach. Def. Peace Econ. 2021, 1–24. [Google Scholar] [CrossRef]
- Ullah, S.; Andlib, Z.; Majeed, M.T.; Sohail, S.; Chishti, M.Z. Asymmetric effects of militarization on economic growth and environmental degradation: Fresh evidence from Pakistan and India. Environ. Sci. Pollut. Res. 2021, 28, 9484–9497. [Google Scholar] [CrossRef] [PubMed]
- Gould, K.A.; Pellow, D.N.; Schnaiberg, A. Treadmill of Production: Injustice and Unsustainability in the Global Economy; Paradigm: Boulder, CO, USA; Routledge: New York, NY, USA, 2008. [Google Scholar]
- Isiksal, A.Z. Testing the effect of sustainable energy and military expenses on environmental degradation: Evidence from the states with the highest military expenses. Environ. Sci. Pollut. Res. 2021, 28, 20487–20498. [Google Scholar] [CrossRef] [PubMed]
- Aye, G.C.; Balcilar, M.; Dunne, J.P.; Gupta, R.; Van Eyden, R. Military expenditure, economic growth and structural instability: A case study of South Africa. Def. Peace Econ. 2014, 25, 619–633. [Google Scholar] [CrossRef] [Green Version]
- Shahbaz, M.; Afza, T.; Shabbir, M.S. Does defence spending impede economic growth? cointegration and causality analysis for Pakistan. Def. Peace Econ. 2013, 24, 105–120. [Google Scholar] [CrossRef] [Green Version]
- Hatemi-J, A.; Chang, T.; Chen, W.-Y.; Lin, F.-L.; Gupta, R. Asymmetric causality between military expenditures and economic growth in top six defense spenders. Qual. Quant. 2018, 52, 1193–1207. [Google Scholar] [CrossRef] [Green Version]
- Mohammed, N.A.L. The Development Trap: Militarism, Environmental Degradation and Poverty in the South. In A World Divided; Routledge: London, UK, 2020; pp. 44–66. [Google Scholar]
- Siacotos, M. Modern Military Weaponry and (un) Sustainable Treatment of the Environment. Commons Puget Sound J. Politics 2020, 1, 2. Available online: https://soundideas.pugetsound.edu/thecommons/vol1/iss1/2 (accessed on 15 September 2021).
- Hooks, G.; Smith, C.L. Treadmills of production and destruction: Threats to the environment posed by militarism. Organ. Environ. 2005, 18, 19–37. [Google Scholar] [CrossRef]
- Heo, U.; Ye, M. Defense Spending and Economic Growth around the Globe: The Direct and Indirect Link. Int. Interact. 2016, 42, 774–796. [Google Scholar] [CrossRef]
- Ram, R. Conceptual Linkages between Defense Spending and Economic Growth and Development: A Selective Review. Defense Spending and Economic Growth; Routledge: London, UK, 2019; Available online: https://www.taylorfrancis.com/chapters/edit/10.4324/9780429040863-2/conceptual-linkages-defense-spending-economic-growth-development-selective-review-rati-ram (accessed on 15 September 2021).
- Topal, M.H.; Unver, M.; Türedi, S. The military expenditures and economic growth nexus: Panel bootstrap granger causality evidence from NATO countries. Panoeconomicus 2021, 2. [Google Scholar] [CrossRef]
- Pesaran, M.H.; Shin, Y.; Smith, R.J. Bounds testing approaches to the analysis of level relationships. J. Appl. Econom. 2001, 16, 289–326. [Google Scholar] [CrossRef]
- Shin, Y.; Yu, B.; Greenwood-Nimmo, M. Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In The Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications; Horrace, W., Sickles, R., Eds.; Springer: New York, NY, USA, 2014; pp. 281–314. [Google Scholar] [CrossRef]
- Hatemi-J, A. Asymmetric causality tests with an application. Empir. Econ. 2012, 43, 447–456. [Google Scholar] [CrossRef]
- Hatemi-J, A.; El-Khatib, Y. An extension of the asymmetric causality tests for dealing with deterministic trend components. Appl. Econ. 2016, 48, 4033–4041. [Google Scholar] [CrossRef]
- Sims, C.A. Macroeconomics and Reality. J. Econometr. Soc. 1980, 48, 1–48. [Google Scholar] [CrossRef] [Green Version]
- Koop, G.; Pesaran, M.; Potter, S.M. Impulse response analysis in nonlinear multivariate models. J. Econ. 1996, 74, 119–147. [Google Scholar] [CrossRef]
- Pesaran, H.; Shin, Y. Generalized impulse response analysis in linear multivariate models. Econ. Lett. 1998, 58, 17–29. [Google Scholar] [CrossRef]
- Hatemi, J.A. Asymmetric Generalized Impulse Responses and Variance Decompositions with an Application. 2011. Available online: https://mpra.ub.uni-muenchen.de/31700/1/MPRA_paper_31700.pdf (accessed on 15 September 2021).
- Greiner, P.T.; McGee, J.A. Divergent Pathways on the Road to Sustainability: A Multilevel Model of the Effects of Geopolitical Power on the Relationship between Economic Growth and Environmental Quality. Socius Sociol. Res. Dyn. World 2018, 4, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Jorgenson, A.K.; Fiske, S.; Hubacek, K.; Li, J.; McGovern, T.; Rick, T.; Schor, J.B.; Solecki, W.; York, R.; Zycherman, A. Social science perspectives on drivers of and responses to global climate change. Wiley Interdiscip. Rev. Clim. Chang. 2019, 10, e554. [Google Scholar] [CrossRef] [Green Version]
- Hooks, G.; Smith, C.L. The Treadmill of Destruction: National Sacrifice Areas and Native Americans. Am. Sociol. Rev. 2004, 69, 558–575. [Google Scholar] [CrossRef]
- Jorgenson, A.; Clark, B. Are the Economy and the Environment Decoupling? A Comparative International Study, 1960–2005. Am. J. Sociol. 2012, 118, 1–44. [Google Scholar] [CrossRef]
- Hooks, G.; Smith, C.L. The treadmill of destruction goes global: Anticipating the environmental impact of militarism in the 21st century. In The Marketing of War in the Age of Neo-Militarism; Gouliamos, K., Kassimeris, C., Eds.; Routledge: London, UK, 2013; pp. 60–83. [Google Scholar]
- Jorgenson, A.; Clark, B.; Kentor, J. Militarization and the Environment: A Panel Study of Carbon Dioxide Emissions and the Ecological Footprints of Nations, 1970–2000. Glob. Environ. Polit. 2010, 10, 7–29. [Google Scholar] [CrossRef]
- Smith, C.L.; Lengefeld, M.R. The Environmental Consequences of Asymmetric War: A Panel Study of Militarism and Carbon Emissions, 2000–2010. Armed. Forces Soc. 2020, 46, 214–237. [Google Scholar] [CrossRef]
- Ahmed, S.; Alam, K.; Rashid, A.; Gow, J. Militarisation, energy consumption, CO2 emissions and economic growth in Myanmar. Def. Peace Econ. 2020, 31, 615–641. [Google Scholar] [CrossRef]
- Ferreira, C.; Ribeiro, J.; Almada, S.; Freire, F. Environmental Assessment of Ammunition: The Importance of a Life-Cycle Approach. Propellants Explos. Pyrotech. 2017, 42, 44–53. [Google Scholar] [CrossRef]
- Bradford, J.H.; Stoner, A. The Treadmill of Destruction in Comparative Perspective: A Panel Study of Military Spending and Carbon Emissions, 1960–2014. J. World-Syst. Res. 2017, 23, 298–325. [Google Scholar] [CrossRef]
- Clark, B.; Jorgenson, A.K. The Treadmill of Destruction and the Environmental Impacts of Militaries1. Sociol. Compass 2012, 6, 557–569. [Google Scholar] [CrossRef]
- Clark, B.; Jorgenson, A.K.; Kentor, J. Militarization and energy consumption: A test of treadmill of destruction theory in comparative perspective. Int. J. Sociol. 2010, 40, 23–43. [Google Scholar] [CrossRef]
- Gould, K.A. The Ecological Costs of Militarization. Peace Rev. 2007, 19, 331–334. [Google Scholar] [CrossRef]
- Zandi, G.; Haseeb, M.; Abidin IS, Z. The impact of democracy, corruption and military expenditure on environmental degradation: Evidence from top six Asean countries. Humanit. Soc. Sci. Rev. 2019, 7, 333–340. [Google Scholar] [CrossRef] [Green Version]
- Fan, H.; Liu, W.; Coyte, P.C. Do Military Expenditures Crowd-out Health Expenditures? Evidence from around the World, 2000–2013. Def. Peace Econ. 2018, 29, 766–779. [Google Scholar] [CrossRef]
- Sohag, K.; Husain, S.; Hammoudeh, S.; Omar, N. Innovation, militarization, and renewable energy and green growth in OECD countries. Environ. Sci. Pollut. Res. 2021, 28, 36004–36017. [Google Scholar] [CrossRef]
Methods | CO2 | AIMP | AFP | MEXP | GDPPC |
---|---|---|---|---|---|
Mean | 0.937 | 2.28 × 109 | 2,028,878 | 2.924 | 936.021 |
Maximum | 1.799 | 5.38 × 109 | 3,047,000 | 4.231 | 2152.216 |
Minimum | 0.404 | 7.37 × 108 | 1,260,000 | 2.342 | 404.235 |
Std. Dev. | 0.447 | 1.09 × 109 | 713,071.9 | 0.462 | 528.237 |
Skewness | 0.670 | 0.610 | 0.001 | 0.941 | 0.916 |
Kurtosis | 2.153 | 2.906 | 1.300 | 3.334 | 2.607 |
Correlation | |||||
---|---|---|---|---|---|
Probability | CO2 | AIMP | AFP | MEXP | GDPPC |
CO2 | 1 | ||||
----- | |||||
AIMP | 0.348 | 1 | |||
(0.017) | ----- | ||||
AFP | 0.894 | 0.134 | 1 | ||
(0.000) | (0.371) | ----- | |||
MEXP | −0.725 | 0.165 | −0.771 | 1 | |
(0.000) | (0.273) | (0.000) | ----- | ||
GDPPC | 0.990 | 0.323 | 0.884 | −0.710 | 1 |
(0.000) | (0.028) | (0.000) | (0.000) | ----- |
Variables | Level | First Difference | Decision | ||
---|---|---|---|---|---|
Constant | Constant and Trend | Constant | Constant and Trend | ||
CO2 | 2.496 (1.000) | −1.188 (0.900) | −5.302 (0.000) | −6.138 (0.000) | I(1) |
AIMP | −2.504 (0.121) | −2.494 (0.329) | −7.053 (0.000) | −6.967 (0.000) | I(1) |
AFP | −0.508 (0.879) | −2.499 (0.327) | −6.950 (0.000) | −6.884 (0.000) | I(1) |
MEXP | −1.886 (0.335) | −3.043 (0.132) | −4.980 (0.000) | −4.921 (0.001) | I(1) |
GDPPC | −1.936 (0.313) | −2.838 (0.191) | −1.763 (0.393) | −5.569 a (0.000) | I(1) |
Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|
0 | −1779.682 | NA | 5.57 × 1030 | 84.984 | 85.191 | 85.060 |
1 | −1578.646 | 344.632 | 1.29 × 1027 * | 76.602 | 77.843 * | 77.057 * |
2 | −1553.269 | 37.460 | 1.33 × 1027 | 76.584 | 78.859 | 77.418 |
3 | −1537.323 | 19.743 | 2.36 × 1027 | 77.015 | 80.325 | 78.228 |
4 | −1498.149 | 39.173 * | 1.61 × 1027 | 76.340 * | 80.684 | 77.932 |
Dependent Variable: CO2t | ||||
---|---|---|---|---|
Selected Model: (4, 3, 4, 4, 4, 4, 4, 4) | ||||
Cointegrating Form | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
∆(CO2)t−1 | −0.0659 | 0.0585 | −1.1277 | 0.3765 |
∆(CO2)t−2 | −0.1628 | 0.0611 | −2.6642 | 0.1167 |
∆(CO2)t−3 | −0.0977 | 0.0535 | −1.8257 | 0.2094 |
∆(AIMP_POS)t | 2.37 × 10−11 | 4.01 × 10−12 | 5.8961 | 0.0276 |
∆(AIMP_POS)t−1 | −2.26 × 10−12 | 5.11 × 10−12 | −0.4420 | 0.7017 |
∆(AIMP_POS)t−2 | 4.06 × 10−11 | 4.15 × 10−12 | 9.7826 | 0.0103 |
∆(AIMP_NEG)t | −4.89 × 10−11 | 2.61 × 10−12 | −18.713 | 0.0028 |
∆(AIMP_NEG)t−1 | 5.72 × 10−11 | 1.68 × 10−12 | 33.966 | 0.0009 |
∆(AIMP_NEG)t−2 | 1.29 × 10−12 | 2.57 × 10−12 | 0.5030 | 0.6649 |
∆(AIMP_NEG)t−3 | −1.64 × 10−11 | 3.46 × 10−12 | −4.7404 | 0.0417 |
∆(AFP_POS)t | −3.44 × 10−8 | 4.09 × 10−9 | −8.4191 | 0.0138 |
∆(AFP_POS)t−1 | −4.04 × 10−8 | 4.72 × 10−9 | −8.5668 | 0.0134 |
∆(AFP_POS)t−2 | 2.94 × 10−8 | 4.19 × 10−9 | 7.0097 | 0.0198 |
∆(AFP_POS)t−3 | −5.10 × 10−8 | 6.42 × 10−9 | −7.9320 | 0.0155 |
∆(AFP_NEG)t | −1.34 × 10−7 | 1.21 × 10−8 | −11.044 | 0.0081 |
∆(AFP_NEG)t−1 | 4.19 × 10−8 | 1.03 × 10−8 | 4.0559 | 0.0558 |
∆(AFP_NEG)t−2 | −9.94 × 10−8 | 1.13 × 10−8 | −8.8376 | 0.0126 |
∆(AFP_NEG)t−3 | −1.89 × 10−7 | 2.17 × 10−8 | −8.7021 | 0.0129 |
∆(MEXP_POS)t | 0.0384 | 0.0075 | 5.0722 | 0.0367 |
∆(MEXP_POS)t−1 | 0.1764 | 0.0107 | 16.4135 | 0.0037 |
∆(MEXP_POS)t−2 | −0.1381 | 0.0090 | −15.2831 | 0.0043 |
∆(MEXP_POS)t−3 | 0.1108 | 0.0185 | 5.9889 | 0.0268 |
∆(MEXP_NEG)t | −0.1645 | 0.0153 | −10.7029 | 0.0086 |
∆(MEXP_NEG)t−1 | −0.2306 | 0.0131 | −17.5103 | 0.0032 |
∆(MEXP_NEG)t−2 | 0.1495 | 0.0096 | 15.5512 | 0.0041 |
∆(MEXP_NEG)t−3 | 0.1018 | 0.0115 | 8.7789 | 0.0127 |
∆(GDPPC)t | −0.0001 | 0.00004 | −2.5821 | 0.1229 |
∆(GDPPC)t−1 | −0.0004 | 0.00007 | −5.5871 | 0.0306 |
∆(GDPPC)t−2 | 0.00004 | 0.00004 | 1.2256 | 0.3451 |
∆(GDPPC)t−3 | 0.0004 | 0.00005 | 8.7882 | 0.0127 |
ECTt−1 | −0.7037 | 0.0512 | −13.7324 | 0.0053 |
Long Run Coefficients | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
AIMP_POS | 0.000007 | 0.000002 | 3.731664 | 0.0649 |
AIMP_NEG | 0.000042 | 0.000009 | 4.719191 | 0.0491 |
AFP_POS | −0.000075 | 0.000012 | −6.072951 | 0.0261 |
AFP_NEG | −0.041776 | 0.004417 | −9.455991 | 0.0110 |
MEXP_POS | −0.125751 | 0.048754 | −2.579299 | 0.1232 |
MEXP_NEG | −0.247768 | 0.022630 | −10.948671 | 0.0082 |
GDPPC | 0.000244 | 0.000073 | 3.334640 | 0.0794 |
C | 0.201434 | 0.017825 | 11.300782 | 0.0077 |
Test Statistic | Value | k |
---|---|---|
F-statistic | 264.120 | 7 |
Significance | I(0) Bound | I(1) Bound |
10% | 2.03 | 3.13 |
5% | 2.32 | 3.5 |
1% | 2.96 | 4.26 |
Methods | Statistics | Probability Value |
---|---|---|
Jarque–Bera Test | 0.305 | 0.858 |
Autocorrelation LM (1) Test | 32.956 | 0.109 |
Heteroskedasticity Test | 0.475 | 0.864 |
Ramsey RESET Test | 0.154 | 0.902 |
Period | CO2 | AIMP_NEG | AIMP_POS | AFP_NEG | AFP_POS | MEXP_NEG | MEXP_POS | GDPPC |
---|---|---|---|---|---|---|---|---|
2022 | 0.023732 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2023 | 0.013883 | 0.009317 | 0.000499 | −0.008886 | 0.002094 | 0.001864 | 0.002737 | 0.009211 |
2024 | 0.017386 | 0.004720 | 0.003433 | −0.014512 | 0.008155 | −0.002463 | 0.001287 | 0.012552 |
2025 | 0.018795 | 0.004093 | 0.004918 | −0.021820 | 0.011603 | −0.000810 | 0.000622 | 0.010152 |
2026 | 0.017703 | 0.006956 | 0.014305 | −0.024944 | 0.015274 | −0.001018 | 0.000545 | 0.006954 |
2027 | 0.019114 | 0.009474 | 0.020195 | −0.026746 | 0.017221 | −0.001093 | −0.000159 | 0.001097 |
2028 | 0.017660 | 0.011080 | 0.024554 | −0.025036 | 0.019989 | 0.000306 | 0.000676 | −0.003378 |
2029 | 0.016434 | 0.011197 | 0.026787 | −0.019827 | 0.021142 | 0.001312 | 0.002671 | −0.003928 |
2030 | 0.014491 | 0.009596 | 0.025409 | −0.013309 | 0.021146 | 0.001907 | 0.004474 | −0.001114 |
2031 | 0.012272 | 0.007395 | 0.022921 | −0.006774 | 0.020808 | 0.001124 | 0.005128 | 0.003558 |
Period | S.E. | CO2 | AIMP_NEG | AIMP_POS | AFP_NEG | AFP_POS | MEXP_NEG | MEXP_POS | GDPPC |
---|---|---|---|---|---|---|---|---|---|
2022 | 0.023732 | 100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2023 | 0.031971 | 73.95665 | 8.492226 | 0.024316 | 7.724729 | 0.428924 | 0.339911 | 0.732669 | 8.300572 |
2024 | 0.042437 | 58.76132 | 6.057007 | 0.668277 | 16.07933 | 3.936670 | 0.529823 | 0.507842 | 13.45973 |
2025 | 0.053943 | 48.50572 | 4.324451 | 1.244654 | 26.31257 | 7.063053 | 0.350441 | 0.327580 | 11.87153 |
2026 | 0.066193 | 39.36708 | 3.976299 | 5.497217 | 31.67552 | 10.01515 | 0.256390 | 0.224342 | 8.988004 |
2027 | 0.079113 | 33.39646 | 4.217709 | 10.36475 | 33.60423 | 11.74954 | 0.198560 | 0.157454 | 6.311298 |
2028 | 0.091295 | 28.82066 | 4.640183 | 15.01709 | 32.75498 | 13.61686 | 0.150231 | 0.123721 | 4.876282 |
2029 | 0.101548 | 25.91327 | 4.966278 | 19.09594 | 30.28613 | 15.34025 | 0.138119 | 0.169165 | 4.090853 |
2030 | 0.109128 | 24.20174 | 5.073553 | 21.95672 | 27.71242 | 17.03816 | 0.150146 | 0.314543 | 3.552727 |
2031 | 0.114711 | 23.04768 | 5.007302 | 23.86400 | 25.42921 | 18.71030 | 0.145486 | 0.484495 | 3.311518 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Khan, H.u.R.; Abbas, S.; Anser, M.K.; Nassani, A.A.; Haffar, M.; Zaman, K. Security Challenges and Air Quality Management in India: Emissions Inventory and Forecasting Estimates. Atmosphere 2021, 12, 1644. https://doi.org/10.3390/atmos12121644
Khan HuR, Abbas S, Anser MK, Nassani AA, Haffar M, Zaman K. Security Challenges and Air Quality Management in India: Emissions Inventory and Forecasting Estimates. Atmosphere. 2021; 12(12):1644. https://doi.org/10.3390/atmos12121644
Chicago/Turabian StyleKhan, Haroon ur Rashid, Shujaat Abbas, Muhammad Khalid Anser, Abdelmohsen A. Nassani, Mohamed Haffar, and Khalid Zaman. 2021. "Security Challenges and Air Quality Management in India: Emissions Inventory and Forecasting Estimates" Atmosphere 12, no. 12: 1644. https://doi.org/10.3390/atmos12121644