In recent years, as cryptocurrency has been recognized by more people and the value of the cryptocurrency has increased, many people make money through mining. It leads to mining becoming popular but also creates serious environmental problems. Mining bitcoin will consume much electricity and thus emits more greenhouse gases such as carbon dioxide, which has caused worldwide environmental issues. This paper will focus on figuring out that mining bitcoin will cause how much carbon emission damage in China during these years. The result of this research will provide the change in carbon emission with time and the prediction of the carbon emission trend caused by bitcoin mining in the future. This result of the article aims to focus the society and the government's attention on the damage mining bitcoin does to the environment and also provide suggestions on the measures the governments should take to reduce the unessential energy cost of bitcoin mining.
Çalışmanın amacı, 03.01.2020 ile 28.02.2022 dönemi için üretim araçlarındaki gelişmenin bir başka veçhesi olan dijitalleşme ile kripto paralara yönelimin hızlanmasının geleneksel borsalara alternatif olup olmayacağını simetrik ve asimetrik nedensellik test yöntemleriyle incelemektir. Bu çerçevede simetrik nedensellik analiz sonuçlarına göre, BTC ve ETH fiyatlarından SP500, NASDAQ ve DOWJ fiyatlarına doğru bir nedensellik ilişkisi saptanmış, aynı zamanda VIX’ten BTC ve ETH’ye doğru bir nedensellik ilişkisi bulunmuştur. Asimetrik nedensellik analizi sonuçlarına göre SP500, NASDAQ, DOWJ ve Altın fiyatlarındaki negatif değişmelerden, BTC fiyatlarındaki pozitif değişmelere doğru bir nedensellik ilişkisi tespit edilmişken, NASDAQ ve DOWJ fiyatlarındaki pozitif değişmelerden ETH fiyatlarının pozitif değişmelerine doğru bir nedensellik ilişkisinin varlığına ulaşılmıştır. Son olarak kripto paralar arasındaki nedensellik ilişkisi sınandığında BTC fiyatlarındaki negatif değişimlerden ETH fiyatlarındaki pozitif değişimlere, ETH fiyatlarındaki negatif değişimlerden BTC fiyatlarındaki pozitif değişimlere doğru bir nedensellik ilişkisi tespit edilmiştir.
Bu çalışmanın amacı, Covid-19 sürecinde bir kripto para birimi olan Bitcoin’in; seçilmiş alternatif yatırım araçları (Brent Petrol, Altın, Etherium) ve seçilmiş göstergeler (Dow Jones, VIX, Covid-19 Google Trend aramaları) ile arasındaki uzun dönemli ilişkileri analiz etmektir. Bu amaç doğrultusunda çalışmada 23/02/2020-16/01/2022 dönemine ait haftalık verilerden yararlanılmıştır. Bitcoin fiyatları ile seçilmiş finansal göstergeler arasındaki uzun dönemli ilişkinin varlığı ise, ARDL Sınır Testi aracılığıyla sınanmıştır. Yapılan analiz neticesinde Bitcoin fiyatı ile seçilmiş finansal göstergeler arasında uzun dönemli bir ilişkinin varlığı tespit edilmiştir. Uzun dönem katsayılarından elde edilen sonuçlara göre, Bitcoin fiyatını en fazla etkileyen göstergelerin, Altın ve VIX endeksi olduğu bulgulanmıştır. Diğer yandan, Bitcoin fiyatları ile Brent Petrol ve Dow Jones endeksi arasında uzun dönemli bir ilişkiye rastlanılmamıştır. Kısa dönem hata düzeltme modelinin sonuçlarına bakıldığında, cari dönemde olası bir şok veya olumsuz senaryo neticesinde meydana gelecek dengesizliğin veya sapmanın bir sonraki dönemde (gelecek haftada veya haftalarda) %51’lik kısmının telafi edilebileceği bulgulanmıştır.
2007 yılında ABD`de başlayan `Mortgage Krizi` çok geçmeden bütün dünya ekonomisini 2008 yılında etkisi altına almıştır. Ülke ekonomilerinde oluşan kriz, finansal piyasalarda çöküş ve dijital ortamlarda gelişmeler sonucunda 2009 yılında ilk kripto para birimi olan Bitcoin’in ön plana çıkmasına yol açmıştır. Günümüzde yüksek piyasa değerine sahip olması, merkezi yönetim sisteminin olmaması, anonimliği ve sağladığı birçok avantajlar bakımından dikkatleri üzerine çekmiş ve geleneksel yatırım araçlarına rakip olup olmaması her zaman tartışma konusu olmuştur. Bu yüzden araştırmacılar tarafından Bitcoin ile ilgili farklı yaklaşımlar ve yöntemler kullanılarak çalışmalar yapılmıştır. Bu çalışmanın temel amacı Bitcoin fiyatı ile Yükselen Piyasa Ekonomileri`nin borsa endeksleri ve diğer önemli değişkenler arasında ilişkinin boyutu ve yönünün belirlenmesidir. Ayrıca Bitcoin`in geleneksel yatırım araçlarına alternatif mi yoksa bir balon mu olduğunu belirlemek amaçlanmıştır. Uygulanan testler sonucunda bazı Yükselen Piyasa Ekonomileri ülkelerinde Bitcoin ile borsa endeksleri ve diğer değişkenler arasında istatistiksel olarak anlamlı ilişki bulunmuştur ve o ülkeler için Bitcoin`in alternatif yatırım aracı olduğu sonucuna ulaşılmıştır.
At present, social capital is considered to be one of the important reasons for promoting economic development and causing regional economic differences, but in the existing research, there is little literature on the impact of regional social capital on enterprises’ green innovation behavior and green total factor productivity (GTFP), so this paper aims to enrich the research in this area. This paper builds a regional social capital evaluation index system and uses the super-SBM model to measure the enterprise GTFP. Then, this paper brings regional social capital, enterprise green innovation and GTFP into a unified framework for the first time and further reveals the quantitative relationship between the three by using OLS and Tobit two-step methods based on the panel data of 30 provinces from 2011 to 2019. The results show that regional social capital has a positive effect on enterprise GTFP and green innovation (except for strategic green innovation output), enterprise green innovation output has a positive role in promoting GTFP, and enterprise green innovation capital investment has a masking effect between regional social capital and GTFP. Furthermore, the expansive study finds that there are differences in the impact of regional social capital on green innovation and the GTFP of heterogeneous enterprises, and financing constraints have a positive regulatory effect on the relationship between regional social capital and the GTFP of state-owned enterprises, while having an inhibitory effect on the GTFP of private enterprises. Fiscal decentralization has a partial mediating effect between regional social capital and enterprise GTFP, while urbanization and CO2 emissions have a masking effect. Additionally, this paper aims to provide a reference for the improvement of regional social capital theory, the strategic choice of green innovation of enterprises, and the high-quality development of the economy.
Tolgahan Tuglu, Canan Dağıdır Çakan, Mehmet Hanifi Ateş, Aleyna Uca
Cryptocurrencies have been attracting a significant amount of attention in the world since they were first launched in 2009. Pretending to be a decentralized finance solution, it brought out a new era in technology called blockchain. Even though the benefits did not come into action in daily routines for many to be aware of, the market and its variety kept growing. On the other hand, there are also a lot of concerns and unpredictability about the future of this technology. Especially the high energy consumption while generating blocks for mining cryptocurrencies and completing transactions is commonly being criticised. In this study, blockchain technology and the basics of mining and validation procedures such as proof of work (PoW) and proof of stake (PoS) processes will be explained, and the environmental effects of bitcoin mining will be investigated. In the perspective of environmental sustainability of cryptocurrencies, the improvement in usage of renewable energy and its side benefits will be overviewed for a better prediction on the blockchain technology future.
This paper examines the dynamic linkages of green bond with the energy and crypto market. The S&P green bond index (RSPGB) is used as a proxy for the green bond market; S&P global clean energy index and ISE global wind energy (RIGW) are used as proxies for the renewable energy market, and; Bitcoin and Ethereum (RETHER) are used as the proxies of the crypto market. The daily prices of these constituent series are collected using Bloomberg from October 3, 2016 to February 23, 2021. We undertake an empirical analysis through the application of three key tests, namely: dynamic conditional correlation (DCC), Diebold and Yilmaz (Int J Forecast 28(1):57-66, 2012. 10.1016/j.ijforecast.2011.02.006), Baruník and Křehlík (J Financ Econom 16(2):271-296, 2018. 10.1093/jjfinec/nby001) model. The DCC reveals no dynamic linkages of volatility from the green bond to the energy and crypto market in the short run. Referring to Diebold and Yilmaz (2012), it dictates that the green bond (RSPGB) is a net receiver while the energy market (RIGW) and cryptocurrency (RETHER) are the largest and least contributors to the transmission of the volatility. Additionally, the Baruník and Křehlík (2018) model confirmed that the magnitude of the total spillover is high in more prolonged than shorter periods, suggesting reduced diversification opportunities. Overall, the present study exemplifies the significance of the green bond market as protection against risk.
The development of hedging strategies using commodity and cryptocurrency has been a topic of academic and practical interest. An optimal strategy increases the efficiency of risk management and minimizes the costs of hedging. This paper examines time-varying optimal hedging ratios for the ASEAN-5 stock market, hedged with gold and bitcoin. The best hedging instrument was determined using regression and DCC-GARCH model. The analyses resulted in hedge effectiveness criteria. The daily data covered the period from January 1, 2019 to December 31, 2021. The findings were robust to the distribution assumption and to the use of DCC-GARCH model in examining different refit. Finally, this study provides an invaluable starting point to examine the dynamic hedging.
The aim of this research is to investigate the causality between Global Economic Political Uncertainty (GEPU) and Geopolitical Risk (GPRT) and Bitcoin Energy Consumption (BTCE). In order to test the stationarity of the variables, the Lee-Strazich unit root test, which takes into account the structural breaks, was used, and the causality relationship between the variables was analyzed with the Hatemi-J (2012) causality test. Monthly data between May 2011 and February 2022 were used in the research. According to the results obtained from the research, geopolitical risk and global economic policy uncertainity are effective on bitcoin energy consumption. In addition, it has been determined that the negative effects of geopolitical risk and global uncertainties are more dominant. The results show that the demand for bitcoin, which is considered an alternative financial asset class, and accordingly bitcoin energy consumption, increases in case of global risks and economic uncertainties.
Due to a lack of focus on China’s financial decentralization system, the existing research does not pay attention to the beneficial contribution of Chinese local governments to carbon emission reduction through their actions in the financial field. In this study, we collected 16 years of data from 30 provinces in China and utilized a two-way fixed-effects model to empirically test the impact of China’s financial decentralization on carbon emission reduction. The regression results show that China’s financial decentralization system has a significant carbon-emission reduction effect. A heterogeneity analysis shows that this effect is common in different regions of China and that fiscal decentralization will negatively moderate it. A mechanism analysis shows that under China’s financial decentralization system, the active intervention of local governments in local finance will significantly upgrade the energy consumption structure and ease the financing constraints of enterprises. The regression results of the spatial econometric model show that the carbon emission reduction effect of China’s financial decentralization still has a spatial spillover effect. Finally, we put forward corresponding policy recommendations.
Today, high-tech industries such as consumer electronics commonly face government rules on carbon emissions. Among the rules, carbon emission tax as well as extended producer responsibility (EPR) tax are two important measures. Using blockchain, the policy makers can better determine the carbon target environmental taxation (CTET) policy with accurate information. In this paper, based on the mean-variance framework, we study the values of blockchain for risk-averse high-tech manufacturers who are under the government's CTET policy. To be specific, the government first determines the optimal CTET policy. The high-tech manufacturer then reacts and determines its optimal production quantity. We analytically prove that the CTET policy simply relies on the setting of the optimal EPR tax. Then, in the absence of blockchain, we consider the case in which the government does not know the manufacturer's degree of risk aversion for sure and then derive the expected value of using blockchain for the high-tech manufacturers. We study when it is wise for the high-tech manufacturer and the government to implement blockchain. To check for robustness, we consider in two extended models respectively the situations in which blockchain incurs non-trivial costs as well as having an alternative risk measure. We analytically show that most of the qualitative findings remain valid.
The concept of blockchain and cryptocurrencies is one of the most popular concepts of recent years. Cryptocurrencies were first introduces with Bitcoin in 2008 and now they have an increasing variety and popularity. Recent developments in technology firms have brought into question whether there is a relationship between Bitcoin and technology indexes. To this end, this study investigates the causality relationship between Bitcoin and technology indexes using monthly data between the years 2016 and 2021 in G7 and E7 countries. To test the causality relationship between the variables, the Hatemi-J (2012) asymmetric causality test was used. Hatemi-J (2012) test reveals that the relationship between bitcoin and technology indexes becomes different for G7 and E7 countries. The results suggest that developed countries affect bitcoin prices while developing countries are affected by Bitcoin prices. The conclusion is that findings point out the existence of an asymmetric relationship between the series for G7 and E7 countries.
This study employs the ADCC-GARCH approach to investigate the dynamic correlation between bitcoin and 14 major financial assets in different time-frequency dimensions over the period 2013-2021, for which the risk diversification, hedging and safe-haven properties of bitcoin for those traditional assets are further examined. The results show that, first, bitcoin is positively linked to risk assets, including stock, bond and commodity, and negatively linked to the U.S. dollar, which is a safe-haven asset, so bitcoin is closer in nature to a risk asset than a safe-haven asset. Second, the high short-term volatility and speculative nature of the bitcoin market makes its long-term correlation with other assets stronger than the short-term. Third, the positive linkage between the prices of bitcoin and risk assets increases sharply under extreme shocks (e.g., the outbreak of COVID-19 in early 2020). Fourth, bitcoin can hedge against the U.S. dollar, and in the long term, bitcoin can hedge against the Chinese stock market and act as a safe haven for the U.S. stock market and crude oil. However, for most other traditional assets, bitcoin is only an effective diversifier.
Bassam A. Ibrahim, Ahmed A. Elamer, Hussein A. Abdou
This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.
This paper aims to analyze the volatility spillover relationship between cryptocurrencies and stablecoins dynamically. Within the scope of the study, the daily closing price data of Bitcoin (BTC), Ethereum (ETH), BNB cryptocurrencies, and Tether (USDT) and USD Coin (USDC) stablecoins covering the period from January 1, 2019 to April 6, 2022 was analyzed using the Q-VAR model. Our results suggest that the volatility spillover between the cryptocurrency and stablecoins increased during the Covid-19 pandemic. Moreover, the direction and severity of volatility spillover between cryptocurrencies and stablecoins are affected by global events. While the relationship between cryptocurrencies and stablecoins themselves is strong, the relationship between each other is weak. Our findings suggest that global events influence the interaction between crypto-assets and that cryptocurrencies and stablecoins can be good diversifiers for each other. These findings have important implications for financial market regulators, portfolio investors, and academic research.
This study aims to investigate the co-movement and Granger causality between Bitcoin prices (BTC) and M2 (cash, demand, and time deposits), inflation, and economic policy uncertainty (EPU) in the U.K. and Japan. It uses monthly data from 31 July 2010 to 30 August 2020 and employs the wavelet coherence method, Toda-Yamamoto, and nonlinear Granger-causality tests. The empirical results show that (i) Bitcoin prices influence M2 and interact with inflation and EPU. In the short term, inflation affects Bitcoin price positively, supporting Bitcoin as an inflation hedged instrument in Japan. Both in Japan and the U.K., the short-term effects of M2 on Bitcoin prices are negative, while EPU's effects on Bitcoin prices are positive, (ii) a bidirectional Toda-Yamamoto Granger causality exists between Bitcoin prices, inflation, and EPU and confirms that M2 affects Bitcoin prices, (iii) a nonlinear bidirectional causality exists between Bitcoin prices and inflation. While Bitcoin prices Granger cause M2 in the U.K. and Japan, inflation shows a nonlinear Granger causality with EPU in Japan. These findings help investors make investment decisions while considering the effects of M2, inflation, and EPU, and monetary authorities and policymakers make policies involving Bitcoin.
Are conventional and sustainable cryptocurrencies effective hedging instruments for high cryptocurrency uncertainty? This paper examines co-movements between conventional (Bitcoin, Ethereum, Binance Coin, Tether) and sustainable (Cardano, Powerledger, Stellar, Ripple) cryptocurrencies and two cryptocurrency uncertainty indices (UCRY price and UCRY policy). Using weekly returns from 1 October 2017 to 30 March 2021, the paper employs the bivariate wavelet coherence method considering three investment horizons, short-term, medium-term, and long-term. The results confirm that conventional and sustainable cryptocurrencies show consistent positive and identical co-movements with both cryptocurrency uncertainty indices at the short-term horizon during COVID-19 and negative co-movement at the medium-term investment horizon, suggesting the short-term hedging ability of dirty/green cryptocurrencies for high UCRY price and policy. Evidence of negative coherences shows that higher cryptocurrency prices and policy uncertainties lead to lower cryptocurrency returns, reflecting the adverse impact of higher uncertainties on the trust of crypto traders and investors. Weak co-movement is found between dirty/green cryptocurrencies and UCRY price/policy indices, which suggests the possible role of dirty/green cryptocurrencies as a weak hedge for UCRY price and policy indices. These findings provide potential avenues to hedge cryptocurrency uncertainties using conventional and sustainable cryptocurrencies across multiple investment horizons.
Energy production is a phenomenon that has always preserved its importance for the history of humanity, as well as where the energy is spent and its consumption are also important. This study examined the causality relationship between Bitcoin energy consumption and Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise has been taken into account to represent technology companies’ stock market. In the analysis, daily price data for the period 12.02.2017-07.02.2021 were used. Toda-Yamamoto (1995) symmetric causality test and Hatemi-J (2012) asymmetric causality test were used for used to determine the relationship between Bitcoin energy consumption and technology companies’ stock values. According to the results of the Toda-Yamamoto (1995) causality test, it has been found that there is a causality from Bitcoin energy consumption to Apple's stock value; according to the Hatemi-J (2012) asymmetric causality test results, it has been determined that there is a causality from Bitcoin energy consumption positive shocks to Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise stock values negative shocks and from Bitcoin energy expenditure negative shocks to Hewlett Packard Enterprise negative shocks. According to the results of the study in general, it is seen that the change in Bitcoin energy consumption has an effect on the firm returns of the companies that sell the necessary tools for bitcoin energy production. From this, it can be commented that bitcoin mining is also effective on the stock returns of technology companies as well as many financial factors.
The present study is a novel attempt to unravel the connectedness of the green bond with energy, crypto, and carbon markets using the S&P green bond index (RSPGB). We consider MAC global solar energy index (RMGS) and ISE global wind energy index (RIGW) as proxies of the energy market and use bitcoin and the European energy exchange carbon index (REEX) for the cryptocurrency and carbon market. Employing the Diebold and Yilmaz (2012), Baruník and Krehlík (2018), and wavelet coherence econometric techniques, we find that the energy market (RMGS) has the highest connectedness derived from other asset classes, and bitcoin (RBTC) has the least connectedness. Concurrently, we find that the risk transmission is heterogeneous in different scales as the short period has less connectedness than the medium and long run. We conclude that the overall diversification opportunity among green bonds, energy stock, bitcoin, and the carbon market is more in the short-run than in the medium and long-run. In summary, our findings on the green bond market will provide investors, portfolio managers, and policymakers with critical insight into ensuring a sustainable financial market.