زینت گلی

Global greenhouse gas emissions have risen from 31,553 million tons of CO2 equivalent in 1990 to 46,187 million tons in 2022. According to the United Nations Intergovernmental Panel on Climate Change (IPCC), since the late 19th century, the Earth’s average temperature has increased by 1.1 degrees Celsius.Every decade since 1960 has been warmer than the previous one, with the last decade being the hottest on record. The warming caused by human activities and greenhouse gas emissions has currently reached about 1 degree Celsius above pre-industrial levels. Over the past two decades, global scientific and political communities have increasingly focused on the issue of global warming and its associated climate changes. The historic Paris Agreement, signed on December 12, 2015, during the 21st Conference of the Parties (COP21) to the UN Climate Change Convention, was a significant step toward combating climate change and addressing the challenges of reducing emissions and investing in a low-carbon, resilient, flexible, and sustainable economy. The agreement, signed by 195 countries, came into force on November 4, 2016. Under the Paris Agreement, countries committed to reducing greenhouse gas emissions to prevent the global average temperature from rising more than 2 degrees Celsius above pre-industrial levels, and to pursue efforts to limit the increase to 1.5 degrees Celsius above pre-industrial levels.Following the agreement, countries through the UN Climate Change Convention asked the IPCC to provide a special report on the impacts of global warming of 1.5 degrees Celsius above pre-industrial levels and related global greenhouse gas pathways. In the IPCC report, supported by 133 researchers, various greenhouse gas emission pathways to achieve the 1.5-degree goal were outlined. Achieving this goal will require significant reductions in greenhouse gas emissions, with a major focus on the energy sector. Four proposed scenarios, which aim to reach net-zero carbon emissions by 2050, predict a sharp decline in the use of fossil fuels. However, the type of fuel and the speed of the transition in fuel consumption vary considerably, especially for coal, oil, and gas, through 2030. Coal faces the most severe reductions, with consumption needing to decrease by 59% to 78% by 2030 compared to 2010. Natural gas has a better outlook, with predictions ranging from a one-third increase to a one-quarter decrease in different scenarios. Oil has the most uncertain future, with the fourth scenario, based on bioenergy combined with carbon capture and storage (BECCS), predicting an 86% increase in oil consumption compared to 2010. Given the uncertain future of oil in these scenarios, analyzing the impact of implementing each of the IPCC's proposed scenarios on OPEC member countries, whose economies are heavily reliant on oil revenues, is crucial. The innovation of this research lies in examining the effects of climate change policies on oil-producing and exporting OPEC countries, including Iran, using a time-series econometric approach, co-integration equations, and a vector error correction model.

Methods and Material:

In this research, to examine the effects of the IPCC scenarios, which are based on reducing global fossil fuel consumption, on OPEC’s oil demand and supply, a time-series econometric approach was used. Co-integration equations were employed to estimate long-term relationships, and the vector error correction model was applied for short-term estimates. Given the significance of reduced demand for OPEC countries, which are economically dependent on oil export revenues, data on the production and price of OPEC oil were used. Additionally, the long-term effects of environmental actions under the IPCC scenarios, which replace fossil fuels with renewable energy by 2030 and 2050, were incorporated into the model using renewable energy price variables. Variables used in the supply and demand functions include OPEC oil production, OPEC oil prices adjusted for the U.S. consumer price index, industrial production indices for developed and emerging countries, and renewable energy price indices. The research data were gathered monthly from 1986 to 2022. OPEC oil price and production statistics were obtained from OPEC, and the U.S. consumer price index data were sourced from the World Bank. The industrial production index (IP) for developed countries was calculated as a weighted average of IP from the U.S., Japan, Germany, France, the U.K., Italy, Canada, Spain, the Netherlands, Sweden, Norway, Belgium, Austria, Denmark, Finland, Greece, Ireland, Portugal, and Luxembourg, with weights based on the GDP share of each country in total GDP. For emerging countries, the IP index was similarly calculated for China, Brazil, India, South Korea, Mexico, Turkey, and Indonesia. The GDP data were obtained from the World Bank, and IP data from the International Monetary Fund. Renewable energy prices were based on the weighted average levelized cost of energy (LCOE) for renewable sources such as concentrated solar power, offshore and onshore wind power, and photovoltaic solar energy. The weights were based on each energy type's share of total renewable energy production, and the LCOE data were published by the International Renewable Energy Agency. Initially, the industrial production indices for developed and emerging countries, as well as the renewable energy price index, were seasonally adjusted.

OPEC adopts two approaches in the global oil market: a strategic approach, where OPEC acts similarly to non-OPEC producers and amplifies the effect of price shocks, and an adaptive approach, where OPEC seeks to balance non-OPEC production changes and stabilize oil price fluctuations. The estimated coefficients indicate that during the study period, OPEC countries, alongside the increase in non-OPEC production, attempted to maintain their market share, often increasing production to force high-cost producers out of the market. This finding is consistent with those of Bog, Pal, and colleagues (2016), who viewed OPEC as a dominant producer seeking to protect market share by limiting competitors like shale oil producers.The results of the model estimation indicate a direct relationship between OPEC oil supply and real oil prices, with a price elasticity of oil supply of 0.22. Additionally, a 1% increase in non-OPEC production leads to a 1.56% increase in OPEC oil production. The price elasticity of oil demand is negative at -0.05, with demand from developed countries having a more significant impact on OPEC oil demand than demand from emerging countries. Furthermore, a 1% decrease in renewable energy prices reduces OPEC oil demand by 0.26%. Therefore, in the pessimistic IPCC scenario, where oil consumption declines by 37%, OPEC’s oil supply could decrease by 40% by 2030.Based on the findings, it is recommended that OPEC regularly monitor the pace of renewable energy development up to 2030 and adjust its strategies accordingly. Although the growth of industrial production in developed countries has a more significant effect on OPEC oil demand, trends in oil imports from China and India, which accounted for about 40% of OPEC’s exports in 2019, versus declining imports from the U.S. and European OECD countries, which have dropped by 40%, should also be considered by OPEC.

Environmental challenges caused by industrial activities has lead to increased concerns on adopting necessary approaches to internalize their damages. In this regard, the government can take advantage of various tools, such as taxation, as one of the best ways to overcome market failures caused by external effects of polluter units. Given the low energy efficiency of the industrial sector, which is derived from the incorrect signaling of the energy carriers prices, and the significant contribution of the sector to the total energy consumption of the country, the industrial sector can be one of the main priorities of policymakers for applying environmental taxes. In the present study, after determining the most energy intensive industrial subsector, the possibility of applying green tax on its energy consumption has been surveyed. Based on emission levels and environmental-friendly characteristics, energy carriers, in this regard, are considered in three categories of electricity, natural gas and oil products. By estimating the direct and cross-price elasticities of energy demand equations, the capacity of applying green tax on these energy carriers has been assessed. To determine these coefficients, the time series data during the period of 1995-2013 were used, and a CVAR model was applied, as such the long-run relationships between the variables were extracted. The results show that the application of green tax on oil products for the sub-sector of “production of other non-metallic mineral products”, which is determined as the most energy intensive industrial sub-sector, can fulfill the environmental objectives of this type of tax. The application of this tax on natural gas, however, may lead to reversible results. Similarly, if policymakers aims to replace these two categories of energy with a clean energy as electricity, the application of green taxes on them can not achieve this environmental goal.

Understanding how the business cycle is formed leads to make more favorable macroeconomic policies in line with economic stability and a reduction in the deviation of economic growth from the long-term path. Economic theories provide a comprehensive guide for economists to identify indicators that affect business cycles. A combination of leading indicators in a composite index can be useful in obtaining future signals from different sectors of the economy. The construction of a composite index requires several steps, and a non-model or model-based approaches should be used for this process. The purpose of this paper is to introduce modeling and non-model approaches for developing a leading composite index to identify the formation of business cycles. The modeling methods used in this paper are vector autoregressive, factor and Markov switching models. From the results of this paper, it can be argued that the model approach is more applicable because of the disadvantages of a non-model approach, such as the use of short and long-term periods of time. Failing to consider the sustainability characteristics of the target variable, ignoring the evolution of the leading indicator over a period of time and the absence of information on short-term deviations from long-run equilibrium are some reasons to use the model approach more than non-model one.

moving on production possibility frontier during the time is production trend and movement that are moved around the production possibilities frontier is concerned the business cycle. Economic variables can be divided in three categories in terms of the impact on the business cycle include Leading, coincident and lagging indicators. the group of economic series that is expected to shift before economic boom and bust changes are leading indicators. they are a sign for future trends of economy. the importance of leading indicators in comparison with other indices are that with the identification of Them can predict economy before entering periods of boom or recession. Since the use of leading single indicator for predicting the target variable according to the different characteristics and different sources recession (based on economic theories and experiences) is dangerous, the use of composite indicators is recommended. The combination of leading indicators into a composite index can be beneficial in receiving signals from different sectors. The leading composite index of leading indicators identified for the Iranian economy by combining 13 indicators of GDP, liquidity, stock index, oil revenues, investment in unfinished buildings were built. To evaluate the performance of composite leading indicator (CLI) and within-sample forecasting GDP growth during the period (2) 1376- (3) 1393 of the Markov switching model was used. Variables including GDP growth as the dependent variable, the first to fourth lag of CLI and GDP growth as independent variables, intercept and CLI as variables and regime change variables were considered. Among the policy recommendations of the study is attention of policy makers to warning indicators, especially in the economic downturn, lack of reliance on a single leading indicator of business cycles according to different sources and considering composite leading indicator for making the economic policies.

In This paper energy intensity has been decomposed by ideal Fisher index into structural and intensity effect. Results show Structural effect doesnt have considerable changes and the amplitude of fluctuations is around 0.95 and 1.06. Structural effect is slightly more than 1, it means economic activities tend to produce commodities which use more energy but the small change in the share of economic activities has trifle effect on higher energy intensity. However Intensity effect which shows changes in energy intensity independent of compound of economic activities has raising trend. Expect in 1982 intense effect is more than one and means more energy has been used for same commodity. Rising trend of intensity effect shows that waste of energy and inefficiency have risen. Trend of aggregate energy intensity, ratio of energy intensity of currentyear to constant year, is like intensity effect and influence of structural effect on it, is negligible.