Impact of Carbon Emission Factors on Economic Agents Based on the Decision Modeling in Complex Systems
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
- 1.
- Carbon Policy and International Companies.
- –
- Increased production costs: Carbon pricing mechanisms, such as carbon taxes or emissions trading schemes, can increase the cost of production for companies that rely heavily on fossil fuels. This can lead to higher prices for consumers and reduced competitiveness in international markets.
- –
- Investment in clean technologies: Carbon policies can incentivize companies to invest in clean technologies and renewable energy sources. This can lead to long-term cost savings and improved environmental performance.
- –
- Changes in global trade patterns: Carbon policies can influence global trade patterns by making it more expensive to produce and export goods from countries with high carbon emissions. This can create opportunities for companies in countries with lower emissions.
- 2.
- Artificial Intelligence and International Companies.
- –
- Optimizing logistics and transportation: AI can be used to optimize shipping routes, reduce fuel consumption, and improve delivery times. This can lead to significant cost savings and reduced carbon emissions.
- –
- Predictive maintenance: AI can be used to predict equipment failures and schedule maintenance accordingly. This can prevent costly breakdowns and improve the overall efficiency of operations.
- –
- Demand forecasting: AI can be used to forecast demand for goods and services, thereby allowing companies to adjust their production and inventory levels accordingly. This can reduce waste and improve resource utilization.
4. Results
5. Discussion: Carbon Emissions and AI in the Context of International Companies
- –
- Conduct empirical studies in different contexts: The current study focuses on international companies. It would be beneficial to test the model in other contexts, such as small- and medium-sized enterprises (SMEs), non-profit organizations, or specific industries. This would allow for a more comprehensive understanding of the model’s applicability and limitations.
- –
- Analyze specific case studies: In-depth case studies of individual companies could provide valuable insights into the proposed model’s strengths and weaknesses. This could involve comparing the model’s predictions with actual company data and then analyzing the reasons for any discrepancies.
- –
- Introduce additional control variables: The current model includes a range of factors that influence company performance. However, there may be other relevant variables that are not currently included. Further research could identify and incorporate these additional variables to improve the model’s accuracy.
- –
- Carry out additional regression, correlation, and variance analysis: Further types of analyses should be conducted as part of analyzing the reliability of the model.
- –
- Consider the impact of external shocks: The model currently focuses on the impact of factors within the company and its environment. However, external shocks, such as economic crises or natural disasters, can also significantly impact company performance. Further research could explore how the model can be adapted to account for these external factors.
- –
- Investigate the role of non-linear relationships: The current model assumes linear relationships between the variables. However, in reality, these relationships may be non-linear. Further research could explore the use of non-linear models to improve the model’s accuracy.
- –
- How can AI be used to develop more effective carbon mitigation strategies?
- –
- How can international companies adapt their business models to a low-carbon economy?
- –
- What are the ethical implications of using AI in the context of carbon emissions?
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Description of Methods | Modeling the Impact on the Company’s Performance Indicators |
---|---|
Picture fuzzy rough sets | Models the axiomatics of building an econometric model |
Modeling the impact of the external environment on the company’s performance indicators that depend on each other | Models the normalization of the values of exogenous and endogenous variables |
System-related one equations | Models checking the time series of variables for stationarity |
ADL model | Models the identifiability of a system of equations |
ADL model system | Models the axiomatics of building an econometric model |
Using the ADL model system for forecasting | Models the normalization of the values of exogenous and endogenous variables |
Endogenous Variables | Exogenous Variables |
---|---|
The company’s profit in t year (i.e., the money that remained in the company at the end of the reporting period after all expenses and taxes were paid and were distributed among the shareholders in the form of dividends), in RUB billion. | The number of integration solutions of the company in t year (i.e., the number of integrations of the company with other services/platforms made in a year, including those where the company developed the product or implemented its existing product), in pcs. |
The company’s revenue in t year (i.e., the total amount of funds received from the sale of all or part of the products, services, and works produced for the year), in RUB billion. | The Central Bank of Russia interest rate in t year (i.e., the market value of shares directly depends on the interest rate of the Central Bank of Russia since the lower the rate, the higher the growth of consumption and investment, and vice versa), in % per annum. |
The company’s estimated value in t year (i.e., the valuation of the company’s value was determined by taking into account all the sources of its financing, such as debt obligations, preferred shares, ordinary shares, etc.), in RUB billion. | The company expenses in t year (i.e., the company’s day-to-day costs for doing business and producing products and services), in RUB billion. |
The price of the company’s shares in t year (i.e., the price per share from the number of sold shares of the company), in RUB/unit. | Inflation in t year (i.e., the percentage of inflation in Russia for the year), in % per year. |
The total investments in the share capital in t year, in % of the total share capital of companies on the market. | The main part of investment project costs in t year (i.e., the capital expenditures intended for investing in companies, such as the cost of purchasing fixed assets, which can range from, for example, buildings, equipment, technologies, etc.), in RUB billion. |
The search engine market share in t year owned by the company, in %. | The number of competitors in t year (i.e., other TNCs and major competitors of the company), in digits. |
The share of employees in the company compared against all of the employees on the market, in %. | The number of employees of the company in t year, in digits. |
The total company asset share compared against all of the companies on the market, in %. | The value of the company’s assets in t year (i.e., the value of the company’s property and cash, including property and other rights that have a monetary value), in RUB billion. |
Scales for Criteria | Picture Fuzzy Numbers | ||
---|---|---|---|
() | () | () | |
Very low (VL) | 0.1 | 0.1 | 0.5 |
Low (L) | 0.2 | 0.2 | 0.4 |
Middle (M) | 0.3 | 0.3 | 0.3 |
High (H) | 0.6 | 0.2 | 0.2 |
Very High (VH) | 0.8 | 0.1 | 0.1 |
C1 | C2 | C3 | C4 | C5 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DS1 | DS2 | DS3 | DS1 | DS2 | DS3 | DS1 | DS2 | DS3 | DS1 | DS2 | DS3 | DS1 | DS2 | DS3 | |
Research and Development (Criterion 1) | - | - | - | H | H | M | M | L | L | VH | H | M | H | L | M |
Commercialization (Criterion 2) | M | M | VH | - | - | - | L | L | H | H | VL | VL | VL | L | L |
Cost (Criterion 3) | H | H | M | H | VH | H | - | - | - | M | VL | VL | L | M | M |
Operational issues (Criterion 4) | M | L | M | H | H | VH | VH | VH | H | - | - | - | VH | H | M |
Functionality (Criterion 5) | H | H | L | H | VH | M | H | H | VH | L | VL | M | - | - | - |
Decision Maker 1 | ||||||||||||||||||||
D1 | D2 | D3 | D4 | D5 | ||||||||||||||||
µ | η | ν | π | µ | η | ν | π | µ | η | ν | π | µ | η | ν | Π | µ | η | ν | π | |
C1 | 0 | 0 | 0 | 0 | 0.3 | 0.3 | 0.3 | 0 | 0.3 | 0.3 | 0.3 | 0.1 | 0.8 | 0.3 | 0.3 | 0.3 | 0.6 | 0.2 | 0.2 | 0 |
C2 | 0.3 | 0.3 | 0.3 | 0.1 | 0.6 | 0.2 | 0.2 | 0 | 0.2 | 0.3 | 0.3 | 0.3 | 0.6 | 0.6 | 0.2 | 0.2 | 0.1 | 0.3 | 0.3 | 0.3 |
C3 | 0.6 | 0.2 | 0.2 | 0 | 0.3 | 0.3 | 0.3 | 0 | 0 | 0.6 | 0.2 | 0.2 | 0.3 | 0.3 | 0.3 | 0.3 | 0.2 | 0.6 | 0.2 | 0.2 |
C4 | 0.3 | 0.3 | 0.3 | 0.1 | 0.6 | 0.2 | 0.2 | 0 | 0.8 | 0.3 | 0.3 | 0.3 | 0 | 0.6 | 0.2 | 0.2 | 0.8 | 0.3 | 0.3 | 0.3 |
C5 | 0.6 | 0.2 | 0.2 | 0 | 0.6 | 0.2 | 0.2 | 0 | 0.6 | 0.6 | 0.2 | 0.2 | 0.2 | 0.2 | 0.4 | 0.2 | 0 | 0.6 | 0.2 | 0.2 |
Decision Maker 2 | ||||||||||||||||||||
D1 | D2 | D3 | D4 | D5 | ||||||||||||||||
µ | η | ν | π | µ | η | ν | π | µ | η | ν | π | µ | η | ν | π | µ | η | ν | π | |
C1 | 0 | 0 | 0 | 0 | 0.6 | 0.3 | 0.3 | 0.3 | 0.2 | 0.3 | 0.3 | 0.3 | 0.6 | 0.2 | 0.2 | 0 | 0.2 | 0.6 | 0.2 | |
C2 | 0.3 | 0.3 | 0.3 | 0.3 | 0 | 0.6 | 0.2 | 0.2 | 0.6 | 0.2 | 0.2 | 0.1 | 0.1 | 0.5 | 0.3 | 0.2 | 0 | 0.3 | 0.3 | |
C3 | 0.6 | 0.6 | 0.2 | 0.2 | 0.8 | 0.3 | 0.3 | 0.3 | 0 | 0.3 | 0.3 | 0.3 | 0.6 | 0.2 | 0.3 | 0.3 | 0.8 | 0.2 | 0.2 | |
C4 | 0.2 | 0.3 | 0.3 | 0.3 | 0.6 | 0.6 | 0.2 | 0.2 | 0.8 | 0.2 | 0.2 | 0.2 | 0 | 0.3 | 0.3 | 0 | 0.6 | 0.2 | 0.2 | 0 |
C5 | 0.6 | 0.6 | 0.2 | 0.2 | 0.8 | 0.1 | 0.1 | 0 | 0.6 | 0.2 | 0.2 | 0 | 0.8 | 0.2 | 0.2 | 0.3 | 0 | 0 | 0 | 0 |
Decision Maker 3 | ||||||||||||||||||||
D1 | D2 | D3 | D4 | D5 | ||||||||||||||||
µ | η | ν | π | µ | η | ν | π | µ | η | ν | π | µ | η | ν | π | µ | η | ν | π | |
C1 | 0 | 0 | 0 | 0 | 0.3 | 0.3 | 0.3 | 0.1 | 0.6 | 0.2 | 0.2 | 0.3 | 0.3 | 0.3 | 0.1 | 0.3 | 0.3 | 0.3 | 0.1 | |
C2 | 0.8 | 0.1 | 0.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0.3 | 0.3 | 0 | 0.1 | 0.1 | 0.5 | 0.3 | 0.2 | 0.6 | 0.2 | |
C3 | 0.6 | 0.2 | 0.3 | 0.6 | 0.2 | 0.2 | 0 | 0.8 | 0.2 | 0.2 | 0 | 0.6 | 0.2 | 0.3 | 0.3 | 0 | 0.3 | 0.3 | ||
C4 | 0.3 | 0.3 | 0.3 | 0 | 0.3 | 0.3 | 0.1 | 0 | 0.6 | 0.2 | 0.2 | 0 | 0 | 0.3 | 0.3 | 0 | 0.3 | 0.8 | 0.2 | 0.2 |
C5 | 0.2 | 0.2 | 0.4 | 0.8 | 0.2 | 0.2 | 0.3 | 0.1 | 0.8 | 0.1 | 0.1 | 0 | 0.8 | 0.2 | 0.2 | 0.1 | 0 | 0 | 0 | 0 |
D1 | D2 | D3 | D4 | D5 | |
D1 | 0.00 | 0.34 | 0.00 | 0.18 | 0.22 |
D2 | 0.34 | 0.37 | 0.26 | 0.00 | 0.20 |
D3 | 0.34 | 0.00 | 0.18 | 0.22 | 0.00 |
D4 | 0.37 | 0.26 | 0.00 | 0.20 | 0.20 |
D5 | 0.21 | 0.26 | 0.35 | 0.18 | 0.00 |
D1 | D2 | D3 | D4 | D5 | |
D1 | 0.20 | 0.34 | 0.00 | 0.18 | 0.22 |
D2 | 0.24 | 0.37 | 0.26 | 0.00 | 0.20 |
D3 | 0.34 | 0.00 | 0.18 | 0.18 | 0.00 |
D4 | 0.37 | 0.26 | 0.00 | 0.19 | 0.19 |
D5 | 0.26 | 0.35 | 0.18 | 0.17 | 0.17 |
B | F | DL | Ps | |
---|---|---|---|---|
C1 | (⎡0.07; 0.15⎦; ⎡0.05; 0.07⎦; ⎡0.05; 0.07⎦; ⎡0; 0.01⎦) | (⎡0.10; 0.10⎦; ⎡0.05; 0.15⎦; ⎡0.01; 0.05⎦; ⎡0; 0⎦) | (⎡0.05; 0.15⎦; ⎡0.05; 0.07⎦; ⎡0.05; 0.10⎦; ⎡0; 0.05⎦) | (⎡0.05; 0.07⎦; ⎡0.05; 0.07⎦; ⎡0.07; 0.10⎦; ⎡0.01; 0.05⎦) |
C2 | (⎡0.17; 0.13⎦; ⎡0.01; 0.05⎦; ⎡0.01; 0.05⎦; ⎡0; 0⎦) | (⎡0.17; 0.13⎦; ⎡0.01; 0.05⎦; ⎡0.01; 0.05⎦; ⎡0; 0⎦) | (⎡0.05; 0.08⎦; ⎡0.05; 0.08⎦; ⎡0.08; 0.11⎦; ⎡0.01; 0.05⎦) | (⎡0.01; 0.17⎦; ⎡0.01; 0.08⎦; ⎡0.05; 0.14⎦; ⎡0; 0.08⎦) |
C3 | (⎡0.07; 0.15⎦; ⎡0.05; 0.07⎦; ⎡0.05; 0.07⎦; ⎡0; 0.01⎦) | (⎡0.15; 0.10⎦; ⎡0.01; 0.05⎦; ⎡0.01; 0.05⎦; ⎡0; 0⎦) | (⎡0.05; 0.15⎦; ⎡0.05; 0.07⎦; ⎡0.05; 0.10⎦; ⎡0; 0.05⎦) | (⎡0.01; 0.05⎦; ⎡0.01; 0.05⎦; ⎡0.10; 0.11⎦; ⎡0.05; 0.07⎦) |
C4 | (⎡0.04; 0.14⎦; ⎡0.04; 0.07⎦; ⎡0.04; 0.09⎦; ⎡0; 0.04⎦) | (⎡0.14; 0.18⎦; ⎡0.01; 0.04⎦; ⎡0.01; 0.04⎦; ⎡0; 0⎦) | (⎡0.04; 0.07⎦; ⎡0.04; 0.07⎦; ⎡0.07; 0.09⎦; ⎡0.01; 0.04⎦) | (⎡0.04; 0.14⎦; ⎡0.04; 0.07⎦; ⎡0.04; 0.09⎦; ⎡0; 0.04⎦) |
C5 | (⎡0.06; 0.11⎦; ⎡0.04; 0.06⎦; ⎡0.04; 0.06⎦; ⎡0; 0.01⎦) | (⎡0.11; 0.16⎦; ⎡0.01; 0.04⎦; ⎡0.01; 0.04⎦; ⎡0; 0⎦) | (⎡0.04; 0.11⎦; ⎡0.04; 0.06⎦; ⎡0.04; 0.08⎦; ⎡0; 0.04⎦) | (⎡0.04; 0.06⎦; ⎡0.04; 0.06⎦; ⎡0.06; 0.08⎦; ⎡0.01; 0.04⎦) |
B | F | DL | P | |
---|---|---|---|---|
D1 | 0.15 | 0.18 | 0.11 | 0.14 |
D2 | 0.23 | 0.16 | 0.12 | 0.09 |
D3 | 0.16 | 0.11 | 0.14 | 0.09 |
D4 | 0.16 | 0.11 | 0.14 | 0.14 |
D5 | 0.13 | 0.12 | 0.09 | 0.09 |
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Didenko, N.; Skripnuk, D.; Barykin, S.; Yadykin, V.; Nikiforova, O.; Mottaeva, A.B.; Kashintseva, V.; Khaikin, M.; Nazarova, E.; Moshkin, I. Impact of Carbon Emission Factors on Economic Agents Based on the Decision Modeling in Complex Systems. Sustainability 2024, 16, 3884. https://doi.org/10.3390/su16103884
Didenko N, Skripnuk D, Barykin S, Yadykin V, Nikiforova O, Mottaeva AB, Kashintseva V, Khaikin M, Nazarova E, Moshkin I. Impact of Carbon Emission Factors on Economic Agents Based on the Decision Modeling in Complex Systems. Sustainability. 2024; 16(10):3884. https://doi.org/10.3390/su16103884
Chicago/Turabian StyleDidenko, Nikolay, Djamilia Skripnuk, Sergey Barykin, Vladimir Yadykin, Oksana Nikiforova, Angela B. Mottaeva, Valentina Kashintseva, Mark Khaikin, Elmira Nazarova, and Ivan Moshkin. 2024. "Impact of Carbon Emission Factors on Economic Agents Based on the Decision Modeling in Complex Systems" Sustainability 16, no. 10: 3884. https://doi.org/10.3390/su16103884
APA StyleDidenko, N., Skripnuk, D., Barykin, S., Yadykin, V., Nikiforova, O., Mottaeva, A. B., Kashintseva, V., Khaikin, M., Nazarova, E., & Moshkin, I. (2024). Impact of Carbon Emission Factors on Economic Agents Based on the Decision Modeling in Complex Systems. Sustainability, 16(10), 3884. https://doi.org/10.3390/su16103884