Voice of Climate: Focus on GGDP
Abstract
Green development has become a global goal for sustainable development. However, traditional GDP is unable to eff ectively
refl ect the level of economic growth of an economy, let alone consider its relationship with the natural environment and ecosystems.
Regarding the fi rst issue, this paper proposes a suitable GGDP calculation method, which includes traditional GDP, the value of natural
resource depletion, the value of environmental pollution, as well as the benefi ts of resource and environmental improvement. We use partial
least squares regression analysis (PLS) to model and accurately quantify the impact of GGDP variables on climate response indicators. The
results show that the selected GGDP method can signifi cantly correlate and refl ect climate change.
Regarding the second issue, this paper uses dynamic multivariate time series models (ARIMAX) and vector error correction models
(VECM) to predict the impact of China’s climate mitigation. Cointegration tests were performed to determine the long-term equilibrium
relationship among these indicators, and residual stationary white noise tests were conducted. The future 10-year GGDP was estimated using
the quadratic curve estimation method, and future changes in climate indicators for the next 10 years were predicted using a multivariate
time series model. The research fi ndings indicate that using GGD P instead of GDP has a positive impact on global climate mitigation.
refl ect the level of economic growth of an economy, let alone consider its relationship with the natural environment and ecosystems.
Regarding the fi rst issue, this paper proposes a suitable GGDP calculation method, which includes traditional GDP, the value of natural
resource depletion, the value of environmental pollution, as well as the benefi ts of resource and environmental improvement. We use partial
least squares regression analysis (PLS) to model and accurately quantify the impact of GGDP variables on climate response indicators. The
results show that the selected GGDP method can signifi cantly correlate and refl ect climate change.
Regarding the second issue, this paper uses dynamic multivariate time series models (ARIMAX) and vector error correction models
(VECM) to predict the impact of China’s climate mitigation. Cointegration tests were performed to determine the long-term equilibrium
relationship among these indicators, and residual stationary white noise tests were conducted. The future 10-year GGDP was estimated using
the quadratic curve estimation method, and future changes in climate indicators for the next 10 years were predicted using a multivariate
time series model. The research fi ndings indicate that using GGD P instead of GDP has a positive impact on global climate mitigation.
Keywords
PLS, ARIMAX, VECM, Residuals, Stationarity and White Noise Test t
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DOI: https://doi.org/10.18686/esta.v10i2.408
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