İktisat Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.12154/2996

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An evaluation of the impact of the pension system on income inequality: USA, UK, Netherlands, Italy and Türkiye
(Springer Science and Business Media B.V., 2024) Verberi, Can; Kaplan, Muhittin; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This study examines empirically the impact of various characteristics of pension systems, in particular their quality and integrity, on income inequality, utilizing micro-level data from the United States, United Kingdom, Netherlands, Türkiye and Italy. To this end, the income inequality model, which includes public pension (or public/private pension mix), age, education, gender, marital status and employment as independent variables, has been estimated using quantile regression. The results provide a number of valuable information on the impact of the pension system on income inequality: (i) Public pension income significantly reduces overall income inequality across almost all inequality groups in all countries, except for the UK and the Netherlands; (ii) Different types of pension systems vary significantly in their redistributive effects on income; (iii) The empirical results also show that the effect of different pension systems on inequality changes by inequality groups significantly.
Exploring the impact of behavioural factors and personality traits on private pension system participation: A machine learning approach
(İstanbul University Press, 2024) Verberi, Can; Kaplan, Muhittin; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This study aims to investigate the effects of personality traits, in addition to basic financial literacy, private pension literacy and behavioural factors on Private Pension System (PPS) participation using machine learning algorithms. The PPS participation model was trained using both random forest and LightGBM algorithms, and the contributions of model inputs in the prediction of pension participation were interpreted using the Tree SHAP algorithms with swarmplots. The data employed in the empirical analysis is survey data collected from the Şırnak province of Türkiye with a sample size of 449. The findings of the study shows that: (i) PPS participation is more likely for females and middle-aged people; (ii) High basic financial literacy has a negative impact on PPS participation; (iii) Extraversion is the key personality trait affecting PPS participation; (iv) Advanced pension literacy has more impact on participation than simple pension literacy: (v) Present-fatalistic tendency is key behavioural factor and it negatively affects PPS; (vi) Present-hedonistic, conscientiousness, future-time orientation, and locus of control tendencies increase PPS participation. Furthermore, the distribution of colours in LightGBM has a greater degree of uniformity in both directions compared with the random forest algorithm. Finally, to increase PPS participation, the results of the study suggest the implementation of the following policy measures: Tailored pension literacy programmes can help to increase pension participation rates. Incentives should be created to prevent narrow-minded behaviour and establish a sense of protection and control around PPS, targeting middle-aged individuals and women.
Balancing growth and sustainability: The impact of Greenfield investment on trade adjusted carbon emissions
(Elsevier, 2024) Raza, Ali; Azam, Kamran; Khan, Asad ul Islam; Badshah, Waqar; Yönetim Bilimleri Fakültesi, İktisat Bölümü
In the last two decades, the surge in carbon emissions has escalated environmental damage and is a major concern globally. Recognized as a significant threat to humanity, unchecked environmental degradation can potentially hinder the achievement of sustainable development. As a result, accurate monitoring of carbon emissions becomes imperative for formulating effective climate policies. Taking into consideration, this study has taken the newly developed consumption-based carbon emissions measure to study the pollution haven hypothesis and examine the link between Greenfield Investment (GFI) inflows to host nations and their environmental impact for 85 developing countries from 1990 to 2020. The results show a positive correlation between Greenfield investment and Consumption-based Carbon Dioxide Emissions (CCO 2 ) in sampled nations. Similarly, energy usage and export damage the environment because developing countries rely on conventional and old methods of energy usage. The results were further analyzed for low, lower middle, and upper middle income countries as well. The subsample outcome shows that Greenfield investment has a more damaged environment in low income countries as compared to lower middle and upper middle income countries. These insights underscore the urgency for developing countries to adopt environmentally conscious policies to attract international investors. It also emphasizes the need for stringent regulations aimed at curbing environmental pollution and complying with the Sustainable Development Goals (SDGs). Similarly, low and lower middle income countries to attract Greenfield investment, may also focus more on strict environmental pollution policies. Industries must be shifted from conventional energy methods to renewable energy sources. Sustainable Development Goals; 7, 12, and 13 can be achieved by host countries, alluring investors to invest in terms of Greenfield in renewable energy resources, which would be used in automobile transportation, to shift industries from conventional energy resources to renewable energy resources. The same Greenfield investment would also be used in bringing efficient machinery for more production in industries with minimal environmental pollution.
Stock market tumble sparks crypto chaos: A crash risk spillover analysis
(Hungarian Central Statistical Office, 2024) Khan, Asad ul Islam; Özcan, Rasim; Abdul Rahman, Mutawakil; Waheed, Abdul; Yönetim Bilimleri Fakültesi, İktisat Bölümü
The study employs an empirical Bayesian estimation approach to examine how the crash risk of the G-7 (United States [US], United Kingdom [UK], Japan, Germany, Canada, and France excluding Italy) and Chinese equity markets affects the crash risk of the top 11 cryptocurrencies. Two crash risk measures were adopted to determine the monthly crash risk of the two types of markets, which are the most appropriate for skewed returns. Four separate models were estimated using the empirical Bayes estimation method because it considers heterogeneity, is more efficient than least squares, and facilitates more accurate coefficient estimation. The results reveal that the German stock market's crash risks are significantly and contemporaneously associated with the crash risk of all 11 cryptocurrencies, indicating that the German equity market is not a reliable diversifier for cryptocurrencies. The crash risks of the US, UK, and Japanese (German and Canadian) equity markets have a positive (negative) impact on the crash risk of cryptocurrency markets with a one-month lag. Generally, lagged crash risks have a more substantial influence on cryptocurrency crash risk, suggesting that historical crashes in equity markets are better predictors of cryptocurrency crashes. The one-month significant delay effect may present arbitrage opportunities because the risk of crashes in stock markets may signal potential crashes in cryptocurrencies one month in advance. A series of robustness checks confirmed the results of the analysis and the validity of our conclusions. These findings suggest that crypto investors and policy-makers should pay attention to historical events in equity markets. Investors and portfolio managers in the cryptocurrency market should monitor unexpected fluctuations in the stock market, particularly significant declines that could result in significant losses in the future.
The triple impact of innovation, financial inclusion, and renewable energy consumption on environmental quality in some emerging economies
(Econjournals, 2024) Kaplan, Muhittin; Abdul Rahman, Mohammed Muntaka; Khan, Asad ul Islam; Vergil, Hasan; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This paper investigates the triple impact of innovation, financial inclusion, and renewable energy consumption on the quality of environment. The study employed data between 2007 and 2019 from selected emerging economies. Using the fixed effect two-step GMM econometric method. The result found that financial inclusion and innovation have a positive relationship with carbon emissions, hence, contributing to the reduction in the quality of the environment. Renewable energy consumption was found to reduce carbon emissions. Similarly, the interactive terms TPT*FIN, FIN*REN, and TPT*REN were all negatively related to carbon emissions. The study recommends that governments should increase financial instruments to support innovation that will enhance environmental quality. Additionally, governments should strengthen their environmental policies. Financial institutions should encourage firms to access green finance solutions. The value and originality of this study is the introduction of the interactive term which throws more light on variables that affect the environment and through which channel. Moreso, there are few works with these interactive terms relative to emerging economies. Third, there are no previous studies that employed the fixed effect two-step GMM to analyze the impact of financial inclusion, technological innovation, and renewable energy consumption on environmental quality.
Empirical analysis of the impact of Turkish bilateral official development assistance on export
(Universitas Islam Indonesia, 2023) Hassan, Arab Dahir; Dil, Esra
Purpose ― The main objective of this study is to explore the relationship between bilateral official development assistance and the export of Turkey to 18 Turkish aid recipient countries between 1998 and 2019. Methods ― The study employs the gravity model of international trade to capture the effect of official development assistance on Turkish export to its aid recipient countries and utilizes Panel data econometric analysis. Findings ― The official development assistance (ODA) remains statistically significant across the models, indicating that ODA is one of the significant drivers of Turkish bilateral trade with the aid recipient countries. Implications ― The study argues that Turkey applied ODA as a foreign policy tool to access new markets in the Middle East, Balkans, Africa, and Asia. Turkish exports to developing countries increased due to the upsurged country's foreign aid donation to its recipients. Originality ― This study deviates from other studies in the literature by empirically examining the relationship between bilateral Official development assistance and the export of Turkey.
From space to place: Mapping poverty in Turkish regions with NASA's global gridded relative deprivation index
(Springer, 2024) Hassan, Arab Dahir; Ibrahim, Mahat Maalim
This study examines the spatial distribution of poverty in Turkish states using zonal statistics techniques. The recently released Global Gridded Relative Deprivation Index (GRDIv1) dataset by NASA has been utilized. The GRDIv1 index contains six pivotal components: Child Dependency Ratio, Subnational Development Index, Infant Mortality Rate, BuiltUp to Non-Built-Up Ratios, VIIRS nighttime lights, and VIIRS Nighttime Lights (VNL) Slope Component. All the components capture various aspects of regional poverty differences. The results show the eastern regions have significantly higher levels of deprivation than the western regions. This disparity is attributed to conflicts, unemployment, and illiteracy in the East, while the West benefits from higher development. The analysis of the ratio of Built-Up Areas to Non-BuiltUp Areas reveals a complex distribution of urbanization and industrialization, with the western Marmara region emerging as a center of development and industrial activity. Moreover, the analysis of Nocturnal Illumination Patterns, based on VIIRS nighttime light data, further confirms the higher levels of development in the west and the deprivation in the east. This study objectively proves that the Eastern region of Turkiye contains areas with much higher deprivation than does central and western regions.
Examining the shifting dynamics of the Beveridge curve in the Turkish labor market during crises
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Babangida, Jamilu Said; Khan, Asad ul Islam; Aysan, Ahmet Faruk; Yönetim Bilimleri Fakültesi, İktisat Bölümü
Following the global financial crisis, an increasing amount of attention has been directed towards examining the Beveridge curve (BC), which indicates the relationship between unemployment and vacancy rates. This research analyzes the unemployment–vacancy rate dynamics in the Turkiye labor market during both the global financial crisis and COVID-19 periods. The findings from this study demonstrate that the labor market exhibits deteriorating efficiency, as evidenced by movement of BC away from the origin. The unemployment and vacancy rates both increase over time, with a leftward (rightward) shift of BC during the global financial crisis (COVID-19) period. The study also reveals that both crises had no significant effect on unemployment–vacancy rate dynamics. In the Turkish labor market, there exists a situation where the vacancy rate is in shortfall of the unemployment level in Turkiye. This creates a positive relationship between these two factors. The labor market in Turkiye experiences inefficiencies as it struggles to generate a sufficient number of jobs to meet the demand from job seekers.
Moderating role of board gender diversity between odd board composition and audit quality
(Johar Education Society Pakistan, 2023) Hassan, Muhammad Zia Ul; Baith, S. M. Labib Abdul; Butt, Jahanzaib Safdar; Khan, Asad ul Islam; Khan, Asad ul Islam; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This study investigates the relationship between odd board structure, board gender diversity, and audit quality in Pakistani firms. The data is collected from Pakistan Stock Exchange’s KSE100 index companies from the year 2016 to 2020. The study employs regression models to analyze the impact of an odd board structure on audit quality, as measured by audit fees. Additionally, the moderating role of board gender diversity on this relationship is examined. The findings reveal that an odd board structure positively influences audit quality, indicating that firms with an odd number of directors pay higher audit fees. However, the study could not find a significant moderating role of board gender diversity. The study recommends the adoption of an odd board structure to enhance audit quality and further emphasizes the importance of promoting board gender diversity to strengthen governance practices especially audit quality in the Pakistani context.
Whether the crypto market is efficient? Evidence from testing the validity of the efficient market hypothesis
(Bank Indonesia Institute, 2024) Iftikhar, Sundas; Khan, Asad ul Islam; Özcan, Rasim; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This study examines the validity of the efficient market hypothesis for the cryptocurrency market. We use the Exponential Generalized Autoregressive Conditional Heteroscedastic approach to examine the presence of different calendar anomalies i.e., the Halloween effect, the day-of-the-week (DOW) effect, and the month-of-the-year effect in the case of Bitcoin, Ethereum, XRP, Tether, and USD Coin. The findings show that there is no strong evidence of the Halloween effect. We find only robust Thursday and Saturday effects in the mean equation. In the case of the month-of-the-year effect, there is only a reverse January effect. More specifically, we note that April and February are statistically significant in the case of Bitcoin and Ethereum, respectively. Results obtained from the variance equations imply that September and October are the least risky months for investors.
What determines public attitudes toward immigration in the Middle East: An analysis at the individual level
(Emerald Publishing, 2024) Saleh, Deena; Vergil, Hasan; Vergil, Hasan; Yönetim Bilimleri Fakültesi, İktisat Bölümü
Purpose: Surveys in Europe show that immigration is more of a challenge than an opportunity for a significant number of people. However, little attention is given to attitudes toward immigration in the Middle East. This paper examines the effects of personal values and religiosity on the anti-immigration attitudes of citizens in the Middle East and North African countries. Design/methodology/approach: Utilizing data from the World Values Survey, we analyze how personal values and religiosity affect anti-immigration attitudes in nine Middle Eastern countries. The data covers individual-level data of 9 MENA countries from the WVS Round 7 (2017–2022). Factor analysis is applied as a data reduction method. Afterward, an OLS regression analysis is conducted on the pooled data. Findings: Anti-immigration attitudes increase with age, education, and religiosity. Personal values such as national pride, support for nationals, and belongingness to one’s country significantly affect anti-immigration attitudes. Furthermore, the importance of religion as a measure of religiosity was found to be positively associated with anti-immigration attitudes. Originality/value: This paper contributes to underexplored literature by investigating how individual-level determinants, such as demographic indicators, personal values, and religious factors, shape anti-immigration attitudes in the MENA context, distinct from European dynamics.
A comparative assessment of frequentist forecasting models: Evidence from the S&P 500 pharmaceuticals index
(Istanbul University, 2023) Muneza, Christian; Khan, Asad ul Islam; Badshah, Waqar; Khan, Asad ul Islam; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This paper compares three forecasting methods, the autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), and neural network autoregression (NNAR) methods, using the S&P 500 Pharmaceuticals Index. The objective is to identify the most accurate model based on the mean average forecasting error (MAFE). The results consistently show the NNAR model to outperform ARIMA and GARCH and to exhibit a significantly lower MAFE. The existing literature presents conflicting findings on forecasting model accuracy for stock indexes. While studies have explored various models, no universally applicable model exists. Therefore, a comparative analysis is crucial. The methodology includes data collection and cleaning, exploratory analysis, and model building. The daily closing prices of pharmaceutical stocks from the S&P 500 serve as the dataset. The exploratory analysis reveals an upward trend and increasing heteroscedasticity in the pharmaceuticals index, with the unit root tests confirming non-stationarity. To address this, the dataset has been transformed into stationary returns using logarithmic and differencing techniques. Model building involves splitting the dataset into training and test sets. The training set determines the best-fit models for each method. The models are then compared using MAFE on the test set, with the model possessing the lowest MAFE being considered the best. The findings provide insights into model accuracy for pharmaceutical industry indexes, aiding investor predictions, with the comparative analysis emphasizing tailored forecasting models for specific indexes and datasets.
Detecting unknown change points for heteroskedastic data
(Dokuz Eylül Üniversitesi, 2023) Başçı, Sıdıka; Khan, Asad ul Islam; Khan, Asad ul Islam; Yönetim Bilimleri Fakültesi, İktisat Bölümü
There are several tests to detect structural change at unknown change points. The Andrews Sup F test (1993) is the most powerful, but it requires the assumption of homoskedasticity. Ahmed et al. (2017) introduced the Sup MZ test, which relaxes this assumption and tests for changes in both the coefficients of regression and variance simultaneously. In this study, we propose a model update procedure that uses the Sup MZ test to detect structural changes at unknown change points. We apply this procedure to model the weekly returns of the Istanbul Stock Exchange's common stock index (BIST 100) for a 21-year period (2003-2023). Our model consists simply a mean plus noise, with occasional jumps in the level of mean or variance at unknown times. The goal is to detect these jumps and update the model accordingly. We also suggest a trading rule that uses the forecasts from our procedure and compare it to the buy-and-hold strategy.
Threat of intervention in cryptocurrency market: West side story of Bitcoin and Ripple
(Bucharest University of Economic Studies, 2023) Aysan, Ahmet Faruk; Isac, Nicoleta; Drammeh, Ousman; Khan, Asad ul Islam; Özcan, Rasim; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This study examines the impact of intervention threats on the price and volume volatility of Bitcoin and XRP. Using the Threshold or GJR-GARCH model, we analyse the relationship between news shocks (representing intervention threats) and the volatilities of Bitcoin and XRP price and volume returns, based on data from January 2014 to April 2021. The results indicate a significant association between news shocks and Bitcoin's price volatility, suggesting that intervention-related news events have a notable impact. However, the relationship between news shocks and XRP's price volatility is insignificant. Notably, XRP's volume returns demonstrate a positive and significant relationship with news shocks, while Bitcoin's volume returns do not exhibit a significant relationship. Additionally, past shocks and conditional variance shocks significantly contribute to the volatility of today's price or volume returns. These findings suggest that Ripple (XRP) may benefit from the implicit threat of intervention, strategically managing its availability to control price surges.
Social behaviour towards tax payment: A survey-based evidence from SADC countries
(Hüzeyfe Süleyman Arslan, 2023) Ibrahim, Mahat Maalim; Khan, Asad ul Islam; Kaplan, Muhittin; Yönetim Bilimleri Fakültesi, İktisat Bölümü
Tax non-compliance and its consequences have become a subject of increasing interest in academic literature and economic forums worldwide. While most studies on this issue focus on developed countries, there is a growing trend to explore understudied developing countries. To fill this gap, we investigated tax evasion drivers in eight Southern African Development Community (SADC) countries, using the round 7 Afrobarometer survey data conducted in 2019-2020. The survey's comprehensive coverage of economic, political, and sociological questions made it one of the most extensive surveys on the continent. We used logistic regression and Empirical Bayesian estimation and found that political legitimacy significantly influences tax evasion behavior in the SADC region. Individuals residing within the SADC are more likely to engage in tax evasion activities when they perceive a lack of access to fundamental services provided by their governments or harbor doubts about the legitimacy of political institutions. Therefore, policymakers in SADC member states should prioritize reviewing and evaluating economic policies, the performance and efficiency of political institutions, and more inclusive governance. We suggest that a strong and legitimate political framework, coupled with effective service delivery, can contribute to reducing tax evasion rates and enhancing public welfare outcomes. Institutional reforms, increased transparency, accountability, and a more inclusive governance system are necessary for fostering a culture of compliance and trust, leading to improved revenue collection.
Fintech adoption in Pakistan: Mobile Wallet Service (MWS) over GDP causality evidence for pre and post COVID-19
(Kinnaird College for Women, 2023) Latif, Muhammad Nouman; Özcan, Rasim
This paper aims to analyze the use of mobile wallet services in Pakistan and how they affect both the economy and people's daily lives. Socioeconomically disadvantaged households in Pakistan will swiftly use mobile wallet services due to the increasing adoption of technology. Socioeconomic considerations are the most commonly used criterion for determining whether someone is underbanked or unbanked. We used data on mobile wallet services (MWS) and GDP from the State Bank of Pakistan for the period of 2013 to 2022. We conducted a causality test to ascertain the relationship between Pakistan's GDP and mobile wallet service. We discovered that the relationship between mobile wallet service and GDP is unidirectional. We then conducted an ordinary least square regression analysis, and the findings showed a positive correlation between GDP and mobile wallet service, confirming the causality. Because it is incredibly user-friendly and convenient for clients, it demonstrates that this service attracts attention from the public. Many low-income individuals use it, especially during the COVID-19 pandemic, as opposed to using standard banking systems. We recommend that financial literacy initiatives be taken seriously in order to promote equal financial inclusion in the economy. Mobile wallet users should be encouraged to keep using the service in order to aid the government in revenue mobilization. Additionally, it prevents the movement of illicit funds or fraudulent activity in the financial sector.
Assessing the impact of financial obstacles on manufacturing firm's capacity utilization: Bayesian approach
(University Indonesia, 2023) Hassan, Arab Dahir; Özcan, Rasim; Özcan, Rasim; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This study investigates the relationship between financial obstacles and the capacity utilization of manufacturing firms. It departs from previous studies by employing a Bayesian linear regression analysis. The results demonstrate the considerable negative effect that financial constraints have on the capacity utilization of manufacturing enterprises, while access to credit lines has a positive ef-fect. The sample consists of 1,494 private manufacturing firms in 31 European and Central Asian countries. Financial obstacles were perceived as a major impediment to business operations by 65% of the enterprises surveyed. Furthermore, 52% of enterprises in the sample have access to loans from financial institutions, while 47% have no access to credit lines. This implies that the manufacturing sector’s capacity to tap into financial market resources and surmount financial barriers both is vital to its survival and presents a significant challenge.
Is the effect of a health crisis symmetric for physical and digital financial assets? An assessment of gold and bitcoin during the pandemic
(Public Library of Science, 2023) Badshah, Waqar; Musah, Mohammed; Khan, Asad ul Islam; Özer, Ercan; Yönetim Bilimleri Fakültesi, İktisat Bölümü
The emergence of the covid-19 health crisis, in this advanced technological era where connections between markets, nations, and economies have grown stronger than ever before, the shock of the COVID-19 pandemic quickly had an impact on both physical and digital financial assets. The Chinese financial market experienced the first consequences of the covid-19 pandemic, then spilled over to other financial markets, including those for cryptocurrencies and the precious metals. This study examines the impact of the covid-19 pandemic on the volatilities of the dynamics of bitcoin and gold. Both assets share some characteristics, such as online trading platforms, however, gold is a tangible financial asset unlike bitcoin, which is digitally generated without any physical form. This study argues that the similarities and differences between bitcoin and gold play major roles in how the covid19 crisis affected their respective dynamics. Using daily data ranging from 9/22/2014 to 1/ 31/2023 and employing ARMA as the mean equation for GARCH model, the impact of the health crisis (covid-19) is examined on the volatilities of the prices and volumes of bitcoin and gold. Empirical evidence points out that, the pandemic has a symmetric impact on the volatilities of bitcoin and gold price returns, causing them to be more volatile. The impact of the covid-19 observed on the volume returns of the assets, however, is asymmetrical. The empirical results give evidence to the role that the vital differences existing between these assets played during the covid-19 pandemic.
A comparative assessment of frequentist forecasting models: Evidence from the S&P 500 pharmaceuticals index
(İstanbul University Press, 2023) Muneza, Christian; Khan, Asad ul Islam; Badshah, Waqar; Khan, Asad ul Islam; Yönetim Bilimleri Fakültesi, İktisat Bölümü
This paper compares three forecasting methods, the autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), and neural network autoregression (NNAR) methods, using the S&P 500 Pharmaceuticals Index. The objective is to identify the most accurate model based on the mean average forecasting error (MAFE). The results consistently show the NNAR model to outperform ARIMA and GARCH and to exhibit a significantly lower MAFE. The existing literature presents conflicting findings on forecasting model accuracy for stock indexes. While studies have explored various models, no universally applicable model exists. Therefore, a comparative analysis is crucial. The methodology includes data collection and cleaning, exploratory analysis, and model building. The daily closing prices of pharmaceutical stocks from the S&P 500 serve as the dataset. The exploratory analysis reveals an upward trend and increasing heteroscedasticity in the pharmaceuticals index, with the unit root tests confirming non-stationarity. To address this, the dataset has been transformed into stationary returns using logarithmic and differencing techniques. Model building involves splitting the dataset into training and test sets. The training set determines the best-fit models for each method. The models are then compared using MAFE on the test set, with the model possessing the lowest MAFE being considered the best. The findings provide insights into model accuracy for pharmaceutical industry indexes, aiding investor predictions, with the comparative analysis emphasizing tailored forecasting models for specific indexes and datasets.
The language of sustainability: Exploring the implications of metaphors on environmental action and finance
(Corvinus University of Budapest, 2023) Napari, Ayuba; Özcan, Rasim; Khan, Asad ul Islam; Özcan, Rasim; Yönetim Bilimleri Fakültesi, İktisat Bölümü
The relationship between humans and the environment is complex. To capture this complex relationship, metaphors/concepts have always been used. The most prominent of these metaphors/conceptions is the limits concept. This views the natural environment in terms of its carrying capacity and contend that human actions must be controlled so as not to overwhelm the environment. For overburdening the environment will result in a collapse of the natural system. The environmental optimists on the other hand discount the carrying capacity contending that human ingenuity and the market mechanism will overcome any temporary environmental problems that may arise. A tempered version of both is the political-ecological class of metaphors/conceptions which emphasize the political, cultural, and economic factors responsible for environmental decay and/or restoration. In this study, the implications of these metaphors/conceptions on environmental action and environmental finance are examined. It is concluded that, the limits conception views environmental action as a top-bottom endeavor and places governmental and multilateral organizations at the center of environmental and climate finance. The neoclassical and technological optimist concepts contend that, the current capitalist structure is well suited to tackle environmental externalities and government policy should encourage eco-innovation preferable through public-private partnerships. The tapestry and the political-ecological class of metaphors envisages a role for central authorities as well as private local individuals with crowdfunding and corporate social/environmental responsibilities along with governmental and multilateral aid and public-private partnerships being some of the main sources of funds for environmental protection and restoration.

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