Accomplishing the Sustainable Development Goal 13 - Climate Action and the Role of the European Union

Kokotović, Filip; Kurečić, Petar; Mjeda, Trina

doi:10.7906/indecs.17.1.14

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Climate change is a major global challenge faced by humanity today and has attracted extensive attention from the international community [1]. Countries have committed to reducing greenhouse gases to address climate change caused by greenhouse gases such as carbon dioxide (CO 2 ) [2,3]. In 2016, 178 parties signed the Paris Agreement, which became the third landmark international legal text to tackle climate change, following the UN Framework Convention on Climate Change in 1992 and the Kyoto Protocol in 1997, shaping global climate governance patterns.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Wang

Zheng²,

Sun

et al. 2021

Mathematics

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Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3 and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.

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Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Wang

Zheng²,

Sun

et al. 2021

Mathematics

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“…Part of the EU SDGs indicator set [2], the greenhouse gas emission indicator monitors the progress made concerning Goal 13, referring to climate action [72]. SDG13 aims to integrate climate change measures into national policies, strategies and planning [2].…”

Section: Methodsmentioning

confidence: 99%

Empowering Sustainable Consumer Behavior in the EU by Consolidating the Roles of Waste Recycling and Energy Productivity

et al. 2020

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The growth in population and economic activities has direct implications on the deterioration of the natural capital, especially when referring to the increase in greenhouse gas emissions. However, improvement is possible by empowering sustainable consumption and production patterns. Through the 2030 Agenda for Sustainable Development, the United Nations call for a mix of economic development, environmental sustainability, and social inclusion. The agenda also provides the instruments needed to track progress, as each Sustainable Development Goal has a set of indicators meant to assess various dimensions of sustainability. Energy productivity is only one of many, but still special because it reflects sustainable consumption behaviors and production patterns. The 2030 Climate Target Plan elaborated by the European Commission consolidates and brings its contribution to the aims of the 2030 Agenda for Sustainable Development by adding weight to the importance of the greenhouse gas emissions target. The objective of this research was to study the relationship between energy productivity, greenhouse gas emissions, biowaste recycling and nominal GDP in the EU in order to highlight the key of a smooth transition towards sustainable consumption behaviors and production patterns. The results show that recycling, greening the economy and energy productivity are the vectors of this transition.

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“…Goal 7: Affordable and clean energy-with the commitment to increase the share of renewable energy consumption [25]; • Goal 11: Sustainable cities and communities-with the incentive to spread the implementation of green buildings [26]; • Goal 13: Climate action-focusing on reducing GHG emissions to limit global warming [27].…”

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confidence: 99%

Are Green Buildings an Indicator of Sustainable Development?

2023

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The world’s population keeps growing together with the construction rate of buildings that need to reduce their environmental footprint in order to mitigate global warming. This paper analyses if the spread of green buildings can be used as an indicator of broader sustainable development. The study is carried out with data taken from Eurostat Database and green buildings directories for 27 EU countries in the 10-year period spanning from 2010 to 2019. The relationship between the indicators is examined through simple linear regressions, and the results confirm the Environmental Kuznets Curve hypothesis: in developed countries, a growing economy (more GDP) is related to an environmental improvement (fewer GHG emissions). In addition, this study proves that the variance of green buildings expresses with a consistent probability the variance of GDP per capita (p-value = 0.0004 and R2 = 0.8475) and the variance of GHG emissions (p-value = 0.0002 and R2 = 0.8825), meaning that green buildings are indeed an indicator of sustainable development. This is due to the upfront cost required to implement advanced construction technologies that ultimately cut GHG emissions during the building lifecycle. This also points out that policy makers should encourage green building implementation through tax relieves and grants.

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Accomplishing the Sustainable Development Goal 13 - Climate Action and the Role of the European Union

Cited by 5 publications

References 29 publications

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Empowering Sustainable Consumer Behavior in the EU by Consolidating the Roles of Waste Recycling and Energy Productivity

Are Green Buildings an Indicator of Sustainable Development?

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