Sustainable development has become the major key phrase for a vast number of academic researches. It is referred to the sustained economic development without destruction of the natural environment. It has been shown by various studies that these the economy and environment are eventually inseparable. Most agencies produce reports on their sustainability practices each year, which increases the awareness of the society and its individual players regarding the best practices and provides feedback on moving towards a sustainable society.
On the other hand, the increasing rate of the average global temperature indicates the growing required effort on controlling the phenomena. Otherwise, its impacts can extend to every aspect of the human life beyond the economic welfare. One solution to slow down the climate change is to regulate the carbon emissions that are produced by the fossil-fuel-dependent industries, in order to stimulate the shift from the carbon-heavy fuels towards clean renewable resources. Carbon markets that are implemented across the European Union (EU) has shown a successful result on reduction of carbon emissions, which is confirmed by the EU data.
In this paper, we provide a system dynamics approach on studying the effects of the carbon market implementation on US industries, particularly, oil and oil-dependent industries which are responsible for a major portion of the carbon emissions. In order for this, we provide a system dynamics model of the factors that are involved in the sustainable development of a society and connect it to the carbon market and oil market models to provide means of understanding possible economic impacts of the carbon market implementation in the US.
Climate change poses a global threat to the sustainable development of human societies. If not controlled, its impacts can threaten a vast range of human life including economic, social welfare, and public health. Most of the human-made part of this phenomenon is caused by the excessive greenhouse gas emissions (GHG), particularly carbon byproducts. Several solutions have been proposed to reduce the carbon emissions. In this paper, we investigate the effectiveness of an Emission Trading System (ETS), with a case study on the European implementation of this approach.
Our approach is based on the system dynamics methodology. First, we perform a literature study on the main sources of carbon emissions, and investigate the key factors involved in the carbon cycle. Then, we extract the casual relations between the derived factors and parameters. On top of the casual model, we build a stock and flow model in which the stock variables are related to their rate variables through a differential equation whose coefficients are time-varying and determined in the model itself. The whole model is reduced to a system of differential equations with variable coefficients, and is solved numerically using methods such as Runge-Kutta. The mathematical relations between the main variables are derived using regression analysis on the available historic data which are used to train the model. For the set of variables where analytic relations cannot be derived or are not suited, look-up tables are utilized.
The main procedure involved in the ETS is providing an Emission Allowance (EA) trading system, by placing a price on the volumes of the emissions. Thereby, financially incentivizing the main entities that emit large amounts of CO2 (or other GHGs in equivalent volumes of CO2) to reduce their emissions. An economic model between the EA Price, Demand and Supply is derived, where the supply is determined according to the regulations (reduced by 1.74% annually), and the demand is proportional to the actual carbon emissions. All main sources of emissions such as the power sector (whose main player is the electricity demand), manufacturing industries and construction, transport sector, aviation, etc., are included in the demand side. For our case study, the data and reports of Eurostat are used and the model is simulated.
A system dynamic model to determine the relations between the emissions production, demand and allowance prices is provided, which implements the method described in the EU ETS. The European data is used to simulate the model. Our simulations show that the EA pricing system can be increasingly effective to control the emissions though the EA prices, by consistently covering more industries (currently only 45% are covered) and reducing the allowance allocations. The possible implications of such a system for the US are investigated.
Combustion of fossil fuels are the main source of man-made Greenhouse Gas (GHG) emissions. Weather it is in power generation or manufacturing industries; weather in ground transportation or aviation, wherever fossil fuels are burnt to produce energy, GHGs (particularly CO2) are emitted to the atmosphere. Petroleum products are one of the main such fuels whose market events can hugely influence the emission control strategies of the industries. In this paper, we provide an extensive model to investigate the effects of oil market variations on the volumes of the carbon emissions.
Based on the system dynamics methodology, we perform a detailed literature survey on the factors involved in carbon emissions. In particular, we find the main variables involved in the decision making process of the power generation, manufacturing, and transportation industries. We build a causal model that describes the relations between these variables and utilize the related historic data, such as for volumes of emissions by sector and resources, emission allowance price (in an Emission Trading System (ETS)), global oil price, demand and supply, and economic growth. The relations between the variables are derived using time-series analysis wherever possible and look-up tables, otherwise. The overall model is reduced to a system of ordinary differential equations that is solved using numerical Euler methods.
Our oil market model is used to simulate several possible events in the oil market that can drastically affect either demand or supply. Moreover, stochastic events are also introduced where the occurrence of changes in the main market variables and the amount of such changes follow a random walk process. Based on our analysis in the carbon emissions, it is noted that the industries can change their short, mid and long term strategies on the portfolio of the natural resources that are utilized. However, the amount of reactions indicate different elasticities based on the type of industries. For instance, power sector as the main source of carbon emissions in the EU can plan for their mid-term portfolio based on the price changes, whereas in the long-term the effect of the EU regulations and goals in terms of the carbon emission (enforced by the ETS) play a more significant role. Our simulation results confirm these findings. Moreover, a detailed case study of related oil market events in 2016 is provided whose effects are simulated in the model to show the carbon emissions changes.
A system dynamics model that combines the oil market and carbon emission model is provided and the effect of stochastic events in the oil market (that result in the oil price changes) are investigated. A case study of 2016 events are provided and possible future events are simulated to investigate the changes in volumes of carbon emissions.
Oil price is a determinant factor in many economic equations. The consistent growth of oil demand indicates the importance of petroleum products in the economic growth of both developing and developed countries. The new market conditions after the introduction of the shale oil and the extent of its influence on determining the oil price indicates a requirement for new oil market models that include new parameters. In this paper, based on the system dynamics methodology, we provide an updated model of the supply and demand of the oil market to explain the market trends. Our model provides the causal relations between the major components of the market including the determinants of the supply and demand. We divide the supply into the OPEC, non-OPEC and US producers. Further, we have extracted the supply of Iran, Saudi Arabia, Libya, Venezuela, and Iraq in the OPEC, and Russia and Syria in the non-OPEC categories in order to be able to further detail the effects of specific events that influenced their corresponding productions. We also provide a detailed case study of the major market events after 2010 that have had consequences on the oil market. Finally, we train the model with the 2014 and 2015 data and simulate and validate the model for 2016 to support our model's performance.
Understanding the oil price changes through the demand and supply dynamics is a challenging problem. Traditional models based on stochastic process and aggregated supply and demand have been successful in explaining the price trends in short periods of time where there is no big changes in either supply or demand sides of oil market. However, these models usually failed to capture the big shocks of oil price in circumstances where there are unexpected influential events, such as reports on the economic growth of the non-OECD members.
In this paper, we propose a revised model of oil price based on the supply and demand dynamics. Particularly, we inspect the effects of unconventional parameters on the oil price, many of which have been ignored in the traditional models. Our model investigates the effects of these expectational parameters on the major factors that shape the supply and demand. Particularly, the oil price is still modeled to follow the supply to demand ratio, however, the supply and demand are replaced with their expected trends in the model. In other words, based on the influential events that can happen in either demand or supply sides, related variables are initiated which subsequently affect the expected trend of supply or demand, and the oil price eventually.
Our model is a system dynamic model that mathematically relates the determinants of the oil price based on the underlying causal relations. The whole model numerically solves differential equations relating the marginal parameters and the main factors including the oil price, demand and supply. We separate the oil supply into US, OPEC and other non-OPEC countries supplies whose parameters differ due to conventional and unconventional resources. Moreover, we separate the oil demand into OECD, Brazil, Russia, India, China (BRIC) and non-OECD countries’ demands due to the difference in the dependence on the economic growth and oil consumption in those group of countries. We build our core model based on regression analysis on the historic data of WTI oil price, OECD and BRIC economic growth and demand, US, OPEC and other countries supply data. Then we train other parameters of the model using the historic reactions of the market to unexpected events that have occurred in the past.
Finally, we simulate the model to analyze a case study of the events occurred in the 2015 including the deceleration of the growth in Chinese economy, speculations on American shale productions, speculations on the OPEC’s decisions, and conflicts in the Middle-East region. Our results show that our model can be used to reproduce the price trends compliant with the past data, and provide predicted trends of supply, demand and their influences on the price.