Technology spillovers and sustainable environment: Evidence from time-series analyses with Fourier extension

Green growth transition and carbon neutrality nexus: A comparative study on the top carbon emitters

This is a comparative study that investigates the role of green growth, green technological innovations, agricultural eco-efficiency and trade openness on carbon neutrality in the top three carbon emitting countries, namely, China, the USA and India, using panel quantile regression with quarterly data for the time period of 2010-2022. The results reveal different findings which have important policy implications. Firstly, carbon emissions vs green growth and carbon emissions vs green technological innovation have a significant U-shaped relationship, which indicate that when green growth and green technological innovations increases, the rate of carbon emissions continues to decline up to a threshold point and start to increase thereafter. Secondly, carbon emissions vs agricultural ecoefficiency has an inverted U-shaped relationship with the carbon emissions, showing that when agriculture eco-efficiency increases the rate of carbon emissions continues to increase up to a threshold point, and starts declining afterwards. Thirdly, trade openness increases the carbon emissions resulting an increase in the environmental degradation and may hinders achieving the carbon neutrality target. The carbon emissions reduction policies such as carbon taxes, promoting green trade, promoting green growth and emission trading schemes must acknowledge and incorporate the interplay among key stakeholders to enhance their effectiveness.

Determinants of ecological footprint in European countries: Fresh insight from Bayesian model averaging for panel data analysis

This study examines the determinants of the ecological footprint of production in European countries from 1992 to 2020. Using partial and semipartial correlation analyses and Bayesian Model Averaging (BMA) approach for the first time, the research identifies key variables affecting ecological footprint. Using Bayesian methods, posterior inclusion probabilities (PIPs) were calculated for each variable's coefficient estimates, revealing their relative importance. Biocapacity, energy consumption, industrialization, financial development, life expectancy, and globalization displayed notably high PIPs, indicating their strong influence on the ecological footprint. In addition, the study employs cointegration tests to examine the long-run relationship between ecological footprint and explanatory variables. The results indicate significant cointegration between these variables across panels, supported by various test statistics. In the Weighted Pooled DOLS estimation, biocapacity, energy consumption, and life expectancy significantly influence the ecological footprint, while industrialization, financial development, and globalization exert a comparatively smaller impact. Researchers and policymakers should consider these determinants for effective sustainable development planning. These findings underscore the intricate interplay of factors shaping the ecological footprint and offer insights for effective policy interventions towards sustainable development.

Forecast of carbon emissions in China based on time lag MGM(1,m,N|τ) grey model

Global climate issues have been gaining international attention in recent years. As the largest developing country and the prime carbon emitter, the Chinese government has proposed a strategic 'double carbon' target for carbon emissions. To predict carbon emissions more accurately, clarify the future supply situation and optimise resource allocation, based on the grey M G M ( 1 , m , N | τ ) model, we introduced and applied the particle swarm algorithm to determine the time lag parameter τ and proposed a new M G M ( 1 , m , N | τ ) grey model. We give a detailed modelling procedure, including calculation steps and intelligent optimisation algorithms, by fully considering the effect of time lag. In this study, this new model is used to simulate and forecast China's carbon emissions from 2010 to 2019 and compare it with other traditional grey models and their improved time-lagged forms. The results show that the new model has significant advantages in prediction accuracy and validity, plus good prediction performance for carbon emissions, which can be extended to more macro and micro energy consumption prediction problems.

Environmental impact of foreign direct investment in Turkey: does the quality of institutions matter? Evidence from time series analysis using the Fourier extension

Since the 1980s, Turkey has experienced a significant increase in both foreign direct investment (FDI) and its ecological footprint (EFP). While FDI is widely acknowledged as a pivotal driver of economic growth, its impact on environmental degradation is multifaceted and debated. Moreover, a country's institutional framework plays a key role in shaping this relationship. Yet, the influence of institutional structures on the FDI-environment nexus is often neglected in current literature. In this study, we investigate the environmental implications of FDI in Turkey from 1984 to 2018, employing time series analysis with a Fourier extension and accounting for institutional quality. Fourier function models give more effective results in modeling structural breaks. We first use Fourier techniques to assess the unit root and cointegration relationship. Upon establishing cointegration, we employ the DOLS estimator, extended with Fourier terms, to determine the long-term coefficients. We then assess the causal relationship between the variables using the Fourier causality test. Our findings indicate that while FDI exacerbates environmental degradation (supporting the pollution haven hypothesis), the interaction term of FDI-institutional quality mitigates this degradation (supporting the pollution halo hypothesis). Given these empirical findings, this study suggests that strengthening Turkey's institutional quality has the potential to amplify the environmental advantages of FDI, alongside its economic benefits.

A step towards achieving SDG 2030 agenda: Analyzing the predictive power of information globalization amidst technological innovation-environmental stewardship nexus in the greenest economies

Through the transition of novel ideas, products, technologies, and business models, info-globalization facilitates the flow and expansion of cross-border information and resources. In the meantime, this stimulates the potential of informational globalization and the internet for environmental and other technological advancements, and assists to the realization of cleaner and greener production and consumption. However, prior studies have completely disregarded this facet of globalization. Thus, this novel study evaluates the role of technological innovation (TIN) and information globalization (ING) in predicting ecological footprints (EFP) and carbon emissions (CO2) in the world's top ten greenest economies. Besides, this study also unveils the moderating role of TIN and ING for environmental sustainability. The novel research employs non-parametric causality-in-quantiles approaches on quarterly data from 1994Q1 to 2019Q4 in order to quantify for causality-in-mean and causality-in-variance, since there may be no causation at first moment, but higher-order interdependencies may exist. The findings revealed that TIN and ING possess significant predictive potential for both ecological footprint and carbon emissions, indicating asymmetric predictability over environmental sustainability. Moreover, TIN and ING asserted a significant interaction role when it comes to predicting pollution levels in chosen countries. Overall, it is essential to note that natural resource conservation and pollution mitigation via green and technical innovation become a dilemma since pollution has no boundaries and will always stoke fires beyond them. The provision of financial and R&D assistance, as well as the use of mass and social media to raise awareness not only in their own regions but also in neighboring countries, might contribute to the achievement of SDG 13 and Cope26's ambition of cutting pollution by 2030.

Special Economic Zone, Carbon Emissions and the Mechanism Role of Green Technology Vertical Spillover: Evidence from Chinese Cities

Although the special economic zones (SEZs) are considered the backbone of rapid economic development in China, it is unclear whether they contribute to green economic development. From the perspective of the localized industrial chains formed as a result of the SEZ policy, this paper aims to analyze and explain how the development of SEZs influences carbon emissions in Chinese cities by promoting green technologies' vertical spillover along the industrial chain. Based on the panel data of 264 prefecture-level cities from 2011 to 2016 and a relatively new and mostly disaggregated city-level MRIO (multi-region input-output) table in China, this paper constructs green technology vertical spillover as a mechanism variable and discusses the influence theoretically and empirically. The results show that the development of SEZs can reduce a city's carbon emissions. More specifically, for every 10 m2 increase in the size of the SEZ area, the carbon dioxide emission can be reduced by 0.882 g per m2 of the city area. Moreover, mechanism analysis shows that the development of SEZs promotes green technology vertical spillover inside the city, through which the SEZs reduce the city's carbon emissions. The mediation effect occupies 21.96% of the total effect. Furthermore, the impact of the development of SEZs on carbon emissions has regional heterogeneity due to the city's industry structure, green technology stocks, and the zones' administrative hierarchies. The finding of this study could provide several important implications for regional green development, especially in China.

The interaction of environmental systems and human development in a time of wild cards. A big data enhanced foresight study

The article contributes to the research discussion around the human-natural and social-ecological systems by identification of a comprehensive list of global trends related to integrated interaction between environmental systems and human development, including climate change, pollution, new quality of economic growth, and challenges for education and healthcare. The foresight study behind it encompasses a combination of quantitative (big data ) and qualitative (expert) methods. This approach allows for a systemic vision of structural shifts, assessing their probability and impact. The outcomes of the study include the list of global environmental and human development trends and factors classified by STEEPV categories, their effects that split into opportunities and threats, and an assessment of their inter-relation and mutual impact. Policy implications focus on possible areas of intervention for sustainable growth.

Analyzing asymmetric effects of cryptocurrency demand on environmental sustainability

When bitcoin (BTC), the first pioneering cryptocurrency was released in 2009, it was considered an apolitical currency. Besides, the possible effect of BTC and other cryptocurrencies on either financial markets or transactions has been widely discussed. However, the environmental effects of cryptocurrency demand have been ignored. Here, this study examines the nexus between cryptocurrencies and environmental degradation by employing standard and asymmetric causality methods. The Toda-Yamamoto and bootstrap-augmented Toda-Yamamoto test results reveal Bitcoin and Ethereum (ETH) excluding Ripple (XRP) have causal effects on environmental degradation. The Fourier-augmented Toda-Yamamoto test results show causal effects running from Bitcoin and Ripple to environmental degradation, whereas no causal effect runs from Ethereum to environmental degradation. The asymmetric causality shows causal effects from the positive shock of Bitcoin demand, negative shocks of Ripple and Ethereum demands to positive shocks of environmental degradation. Further discussions and policy implications are provided in the relevant sections of this study.

A Study on the Sustainable Relationship among the Green Finance, Environment Regulation and Green-Total-Factor Productivity in China

Exploring the mechanism and constraints of Green Finance on high-quality economic development is of great significance to achieve the strategic goal of carbon peak and carbon neutral. Based on the panel data of 30 provinces in China from 2009 to 2019, this paper uses the epsilon-based measure model and entropy method to measure the total factor rate of green economy and the development level of green finance. It then brings green finance, technological innovation, industrial structure upgrading, environmental supervision and high-quality economic development into a unified research framework for the first time. By constructing a panel two-way fixed effect model, regulatory intermediary effect model and threshold effect model, this paper empirically tests the action mechanism and constraints between green finance and high-quality economic development. The results show that: (1) The spatial evolution of green finance in China presents a gradient decreasing pattern from east to middle to west, coastal to inland, and the spatial evolution presents an obvious southwest-northeast pattern. (2) Green finance does have a significant role in promoting high-quality economic development, in which technological innovation and industrial structure upgrading play a part of the intermediary role. This conclusion is still valid under the robustness test of lagged explanatory variables and after the possible endogenous problems are alleviated by the difference-in-difference model (DID). (3) Environmental regulation plays a non-linear regulatory role in the relationship between green finance and high-quality economic development, and there is a single threshold value. Too high intensity of environmental regulation will weaken green finance, resulting in the innovation compensation effect being more diminutive than the circular cost effect. At this time, the high-quality economic development presents a state of diminishing marginal benefits.