Exploring Indonesia's CO2 Emissions: The Impact of Agriculture, Economic Growth, Capital and Labor

This study aims to eradicate poverty by utilizing the manufacturing industrial sector, which cannot be separated from the influence of investment value and labor absorption. The analysis methods used are multiple linear regression and Vector Autoregression (VAR). The study employs quarterly secondary data from 1999 to 2022. The results of the analysis show that labor and investment partially have a significant and positive effect on the growth of the manufacturing industry. Moreover, by using a bivariate causality test, this study proves the existence of a two-way causal relationship between the economic growth of the manufacturing industry and poverty. Additionally, the study also analyzed the response of the independent variable to the dependent variable using Impulse Response (IRF) and Variance Decomposition (VD). It can be concluded that the economic growth of the manufacturing industry responds negatively to poverty, and poverty responds negatively to the economic growth of the manufacturing industry until both reach a balance. The contribution made by each variable in forming the value of that variable is different from one another. The implementation of good governance is highly expected in efforts to eradicate poverty in Indonesia, one of which is through increasing the economic growth of the manufacturing industry and then creating useful programs to increase investment and employment.

Section: Introductionsupporting

confidence: 57%

Analysis of the Influence of Investment and Labor on Poverty Levels Through the Growth of the Indonesian Manufacturing Industry

Balqis,

Syahnur,

Ernawati

2024

“…In developing nations, where industrialization and economic growth may be priorities, the positive relationship suggests that efforts to boost labor productivity or economic activities might come with an environmental cost. Similarly, Maulidar et al [54] show that increased labor leads to increased CO2 emissions in Indonesia.…”

Section: Pairwise Granger Causality Testmentioning

confidence: 90%

“…[3], Maulidar et al [54], and Adebayo et al [55]. One of its key strengths lies in its ability to capture both short-term and long-term effects, providing a comprehensive understanding of the dynamic relationships between variables.…”

Section: Autoregressive Distributed Lag (Ardl)mentioning

confidence: 99%

Enhancing Environmental Quality: Investigating the Impact of Hydropower Energy Consumption on CO2 Emissions in Indonesia

Maulidar,

Fadila,

Hafizah

et al. 2024

Self Cite

Achieving sustainable environmental quality has become a critical global issue, necessitating the reduction of carbon dioxide (CO2) emissions and greenhouse gas (GHG) emissions to mitigate environmental pollution. Hydropower energy has the potential to play a significant role in this effort by providing a clean, renewable energy source that can help reduce reliance on fossil fuels and decrease CO2 emissions. This study examines the dynamic impact of hydropower energy consumption, economic growth, capital, and labor on Indonesia's CO2 emissions from 1990 to 2020. Applying the Autoregressive Distributed Lag (ARDL) method, the findings demonstrate that hydropower energy consumption has a negative effect on CO2 emissions in both the short and long term, indicating that increasing hydropower energy consumption leads to a reduction in CO2 emissions. Conversely, labor exhibits a positive influence on CO2 emissions in both the short and long term, suggesting that a rise in labor contributes to higher levels of CO2 emissions in Indonesia. Furthermore, the Granger causality analysis reveals a bidirectional relationship between CO2 emissions and hydropower energy consumption. The robustness of ARDL results is confirmed through additional tests using Fully-Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regressions (CCR) methods. The findings underscore the importance of promoting sustainable hydropower energy for effective environmental management in Indonesia. Policymakers should prioritize investments in sustainable hydropower infrastructure, encourage the adoption of energy-efficient technologies, and develop a skilled workforce to mitigate the environmental impact of increased labor force participation.

“…Climate change can be described as one of the most pressing challenges of our time [1,2]. Undeniably, global warming is caused by human activities, and climate scientists stress the urgency for immediate action [3].…”

Section: Introductionmentioning

confidence: 99%

Toward a Greener Future: Investigating the Environmental Quality of Non-Green Trading in OECD Countries

Van Ledden,

Can,

Brusselaers

2024

International trade is recognized as a key contributor to environmental degradation. However, researchers and policymakers do not distinguish between the trade of green and non-green products. The Non-Green Trade Openness Index was developed to examine the effect of non-green products on environmental quality. This study examines the effect of trading non-green products on environmental quality for 37 member countries of the Organization for Economic Co-operation and Development (OECD) from 2003 to 2016 in the context of the Environmental Kuznets Curve (EKC) framework. The results of the long-run estimation techniques employed (FMOLS, DOLS, Fixed and Random effects) confirm the EKC hypothesis and show a negative relationship between the Non-Green Trade Openness Index and greenhouse gas emissions, which serves as a proxy for environmental quality. This implies that when there is more non-green trade in OECD member countries, greenhouse gas emissions within these countries decrease. Dividing green and non-green products is important, empowering practitioners and policymakers to make informed choices and define a strategy for a sustainable future. Additionally, policy recommendations are provided to support policymakers in their efforts to combat climate change.