Definition and Examples of Parallelism in Grammar

In English  grammar, parallelism is the similarity of structure in a pair or series of related words, phrases, or clauses. Also called parallel structure, paired construction, and  isocolon. By convention, items in a series appear in parallel grammatical form: a noun is listed with other nouns, an -ing form with other -ing forms, and so on. Kirszner and Mandell point out that parallelism adds unity, balance, and coherence to your writing. Effective parallelism makes sentences easy to follow and emphasizes relationships among equivalent ideas (The Concise Wadsworth Handbook,  2014). In traditional grammar, failure to arrange related items in parallel grammatical form is called faulty parallelism.   Etymology From the Greek, beside one another Examples and Observations Buy a bucket of chicken and have a barrel of fun.(Slogan of Kentucky Fried Chicken)How vain it is to sit down to write when you have not stood up to live!(Henry David Thoreau, A Year in Thoreaus Journal: 1851)The loss we felt was not the loss of ham but the loss of pig.(E. B. White, Death of a Pig. The Atlantic, January 1948)When you are right you cannot be too radical; when you are wrong, you cannot be too conservative.(Martin Luther King, Jr., Why We Cant Wait. Signet, 1964)Immature poets imitate; mature poets steal.(T.S. Eliot, Philip Massinger, 1920)It took a man like Madiba to free not just the prisoner, but the jailer as well; to show that you must trust others so that they may trust you; to teach that reconciliation is not a matter of ignoring a cruel past, but a means of confronting it with inclusion and generosity and truth. He changed laws, but he also changed hearts.(President Barack Obama, speech at the memorial service for former South African president Nelson Mandela, D ecember 10, 2013)After a few miles, we drove off a cliff.It wasn’t a big cliff. It was only about four feet high. But it was enough to blow out the front tire, knock off the back bumper, break Dad’s glasses, make Aunt Edythe spit out her false teeth, spill a jug of Kool-Aid, bump Missy’s head, spread the Auto Bingo pieces all over, and make Mark do number two.(John Hughes, Vacation 58. National Lampoon, 1980)New roads; new ruts.(Attributed to G. K. Chesterton)Hes quite a man with the girls. They say hes closed the eyes of many a man and opened the eyes of many a woman.(Telegraph operator to Penny Worth in Angel and the Badman, 1947)They are laughing at me, not with me.(Bart Simpson, The Simpsons)Voltaire could both lick boots and put the boot in. He was at once opportunist and courageous, cunning and sincere. He managed, with disconcerting ease, to reconcile love of freedom with love of hours.(Attributed to Dominique Eddà ©)Truth is not a diet but a condiment.( Attributed to Christopher Morley)Some of the people said that the elephant had gone in one direction, some said that he had gone in another, some professed not even to have heard of any elephant.(George Orwell, Shooting An Elephant. New Writing, 1936)Our transportation crisis will be solved by a bigger plane or a wider road, mental illness with a pill, poverty with a law, slums with a bulldozer, urban conflict with a gas, racism with a goodwill gesture.(Philip Slater,  The Pursuit of Loneliness. Houghton Mifflin, 1971)Unlike novelists and playwrights, who lurk behind the scenes while distracting our attention with the puppet show of imaginary characters, unlike scholars and journalists, who quote the opinions of others and shelter behind the hedges of neutrality, the essayist has nowhere to hide.(Scott Russell Sanders, The Singular First Person. The Sewanee Review, Fall 1998)O well for the fishermans boy,That he shouts with his sister at play!O well for the sailor lad,That he sing s in his boat on the bay!(Alfred Lord Tennyson, Break, Break, Break, 1842)[Todays students] can put dope in their veins or hope in their brains. . . . If they can conceive it and believe it, they can achieve it. They must know it is not their aptitude but their attitude that will determine their altitude.(Rev. Jesse Jackson, quoted by Ashton Applewhite et al. in And I Quote, rev. ed. Thomas Dunne, 2003) Effects Created by Parallelism [T]he value of parallel structure goes beyond aesthetics. . . . It points up the structure of the sentence, showing readers what goes with what and keeping them on the right track.(Claire K. Cook, Line by Line. Houghton Mifflin, 1985)Several studies have shown that in conjoined structures, even without ellipsis, parallelism of many types is helpful to the processor,  in that the second conjunct is easier  to process if it is parallel to the first in some way . . ..(Katy Carlson,  Parallelism and Prosody in the Processing of Ellipsis Sentences.  Routledge, 2002) Parallelism has the potential to create rhythm, emphasis, and drama as it clearly presents ideas or action. Consider this long, graceful (and witty) sentence that begins a magazine article on sneakers: A long time ago—before sneaker companies had the marketing clout to spend millions of dollars sponsoring telecasts of the Super Bowl; before street gangs identified themselves by the color of their Adidas; before North Carolina States basketball players found they could raise a little extra cash by selling the freebie Nikes off their feet; and before a sneakers very sole had been gelatinized, Energaired, Hexalited, torsioned and injected with pressurized gas—sneakers were, well, sneakers.[E.M. Swift, Farewell, My Lovely. Sports Illustrated, February 19, 1990] First note the obvious parallelism of four clauses beginning with the word before and proceeding with similar grammatical patterns. Then note the parallel list of sneaker attributes: gelatinized, Energaired and so on. This is writing with pizzazz. It moves. It almost makes you interested in sneakers! Of course you noticed the nice bit of word play—the sneakers very sole.(Lauren Kessler and Duncan McDonald, When Words Collide: A Media Writers Guide to Grammar and Style, 7th ed. Thomson Learning, 2008) Pronunciation: PAR-a-lell-izm

The Works Of John Dewey And Paulo Freire - 1591 Words

Maggie Duguid Final Paper EDF 3604-601 4/28/2015 I found the works of John Dewey and Paulo Freire to be highly impactful. However, I only found them to be truly insightful when compare with the textbook, School and Society. A lot of the concepts that Dewey and Freire discuss need further explanation, which the book provides. Similarly, I found that that textbook alone was very difficult to work with. Like most textbooks, I found it to be very static. With the help of the other readings, certain aspects became much clearer. I should also add that I have never been good at dissecting the work of another person. I do much better dealing with working with the text as a whole. For many of the topics the three readings dealt with, however, I needed to take it apart in order to get the fully comprehend the meaning of the texts. In Experience and Education, Dewey works to lay down a foundation defining what he calls progressive education. His book was written as a response to educations that were still using the traditional education system and actively opposed progressive education. Dewey didn’t write the book to attack or belittle traditional education; he actually wrote it to develop a positive and constructive educational system (Dewey, 20). He wanted to convince people to stop focusing on the labels of education and focus on the how good of an education the students were receiving (Dewey, 90). Dewey felt that at the time education was solely a transfer of knowledge from theShow MoreRelatedCritical Pedagogy And The Reggio Emilia1533 Words   |  7 Pagespedagogy is. Critical pedagogy stems from the idea that there is an unequal social structure in our society which is based upon class, race and gender. It has been formulated and influenced by many psychologists and philosophers including John Dew ey and Paulo Freire. Critical pedagogy is an attempt at freedom from oppression through democratic critical thinking. 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A Heated Classroom Discussion Affirmative Action Example...

Responding to a Heated Classroom Discussion: Affirmative Action Example The case involves a heated classroom discussion where a teacher, Jeffery Moran opens a discussion on Affirmative Action in the class and the students like Rikki Johnson connected the topic to racism and gender discrimination. Soon the discussion lost its essence and hot moments were created in the class due to radical opinions of the students on the topic. CASE ANALYSIS Question1. What are the situational characteristics relevant to the case? a. Specific context of teaching learning situation Factors affecting Teaching – Strength of the class – 350 Seating arrangement - Lecture hall sloped down steeply from entry doors at top to a stage podium at bottom. Number of lectures duration - 2 lectures per week of 50 minutes duration each Attendance per class - Two third of the strength Factors affecting Learning – State University – located in rural area of Midwestern United States encouraged diversity and civility due to which many white students perceive themselves to be victim of reverse discrimination in the college admission process. Campus Environment – incidents of harassment were observed on campus due to which certain interest groups were formed who criticized the administration for their inadequate response. To protect the students from religious, political ideological discrimination state legislators were in the process of passing the â€Å"Academic Bills of Rights†. b. 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Process For Improvement Of Time Management - 1439 Words

Process for Improvement of Time Management Good leadership involves many characteristics. Time management is one characteristic in leadership, which is hard for many nurses to accomplish. Time management involves proper planning, implementation of the plan, and excellent organization of time without wasting time (Said, 2014). Improvement in time management skills is something every good leader should strive to achieve. The importance of time management is something that nursing students understand. As a nursing student, as well as a mother, a wife, and full time nurse, time management is something that just has to happen, or nothing gets done. People with time management skills are able to perform multiple tasks within a certain amount of time (Ebrahimi et al., 2014). Multitasking is just part of everyday, in the life of a nursing student, but I would like to increase the effectiveness of my multitasking, by improving my time management skills at work. 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Usage of Big Data in Business Organizations-Samples for Students

Question: Discuss about the Uses of Big Data in Business Organizations. Answer: Introduction: The method of research into massive amounts of data to expose hidden patterns and correlations termed as Big data analytics. Big data is a term for massive data sets having large, more varied and complex structure with the obstacles of storing, analysing and visualizing for further methods of outcomes. Big data could supply scopes for small businesses, micro industries, independent traders as these could proceed for big corporations. When the average small business has low self-originated data than large corporate such as Apple, Microsoft or Google, it does not interpret that big data is limitless. Big data has more suitability to small businesses, as they are commonly more flexible and has ability to respond more instantly on data-driven insights (Gandomi and Haider, 2015). The big data implementations are required to be analysed and incorporated as appropriately as possible. The companies both large and small are starting to utilize big data and relevant analysis approached to gain data to better support of the company and serve the customers. This research paper represents an outline of content of big data, objectives, opportunities, processes, advantages and issues. The customer trend analysis, concepts of predictive modelling and data mining are going to discussed in the below section of the research report. Project Objectives: The big data analysis through customer transaction analysis and market structure analysis are executed. The global business trends help to shape future business research directions according to the results of past. Additionally, the data-driven business analytics and intelligence are highly applied and these could leverage scopes specified by the abundant data as well as analytics based on domain requirements in many critical and high-influencing application fields (Russom, 2011). A reputed organization considers relevant information and corresponding decision rights for the welfare of the company. The aim of the business project is to generate, transfer and analyse the big data for optimizing cross-functional cooperation and enhancing factors behind domain expertise. Project Scope: Business analytics and intelligence have deep relevance with big data analysis that is gradually becoming crucial in business communities since last five decades. The scopes associated with big data in various organizations have originated significant interest in business analytics. Big data management and warehousing is supposed to be the foundation of Business management. Simple graphics, data characteristics, data segmentation and data analysis are considered as big data mining techniques. Well-established management authority focuses on data-driven predictive modelling and various business applications (Sharda, Delen and Turban, 2013). The velocity, variety, variability, value and volume (5V) of big data help to increase the quality of decision-making (Chen and Zhang, 2014). Use of big data enables authority and managers to decide planning based on evidence rather than intuition. Because of this reason, big data has potentiality to bring revolution in management. Big data is a currently growing technology in the market that can bring vast benefits to the business organizations. The scopes are the digitisation and interlinking of analogue and unstructured data such as social media. Social media information, mobile, cloud and big data technologies converge to solve requirements of business strategy and to get the actual information to reach to consumers quickly by maintaining validity of external big data and reliability of relationships among data elements (Waller and Fawcett, 2013). Current technologies in data access and analytics lend themselves to innovatively attacking challenge of intelligent and proactive col laboration in these disciplines and tool sets. Business administrators could correlate past sales activities and demographics as well as forecasting the demand of products (Zikopoulos and Eaton, 2011). Literature Review: Big data is defined as huge amount of data that needs modern technologies and architectures so that it becomes possible to extract values by capturing and analysing data handling methods. Because of such huge size of data, it becomes very tough for performing effective analysis using the traditional techniques. It is essential for different obstacles and problems related in granting the technology brought under light. Big data technology along with its importance in the world and existing projects that are effective and crucial in transforming the idea of science into big science (Sagiroglu and Sinanc, 2013). Big data analysis reveals the forms, nature and philosophical bases in details. The big data is framed technically, economically, ethically, spatially and philosophically (McAfee, Brynjolfsson and Davenport, 2012). Big data is generally primary and quantitative in nature. It has four major qualities that are Captured, Exhausted, Transient and Derived. Big data analytics provides business abilities to gather customer data apply analytics and quickly detect potential problems. Therefore, big data analysis is becoming more and more crucial to businesses as business is the key point to obtain better data context. Mark Gallagher, the managing director of CMS Motor Sports Ltd. has developed the theory of The Data Driven Business of Winning. Driven by specialized analytics systems and software, big data analytics could focus the way to different benefit utilities involving new revenue scopes, marketing that are more effective, better customer service, enhanced operational efficiencies and competitive advantages over rivals. Data scientists and statisticians handle big data analytics for predictive modelling (Mithas et al., 2013). Useful data resources involve traditional and contemporary data, transforming activities such as identifying markets, market trend, market value and customer satisfaction. Social media, browser logs, text analytics, in-house data as well as large and public datasets such as survey data are the major resources of big-data for the business organisations. Some of the processes used to gather big data involve customer surveys, analysis of customer browsing histories and preference of social media (Liebowitz, 2013). Other ways big data is gathered are sensors utilised in public transportation and credit card spending histories. After collection of big data, it is typically in high capacity hard drives and on the cloud. Big data is used in major decision making for helping a growth of business and achievement of revenue targets. Previously, big data were mostly utilized on-premises especially in big management organisations. However, cloud platform vendors like Amazon Web Services (AWS) and Microsoft have made it easier to establish and manage Hadoop clusters in the data storing clouds (Talia, 2013). With the help of their support, the distribution of framework of big data has become easier. Potential drawbacks may trip up business organizations on initiatives of big data analytics. Therefore, it involves a scarcity of internal analytics skills of the company. To fill up the pitfalls, business and financing companies are hiring experienced data scientists, analysts and data engineers. Organizing big data and making big data sources connected with resources is a critical task. Every department of a company from marketing to finance or from production cell to analytics uses big data for well-informed decisions. Connected big data resources bring more visibility and transparency in any business organization by more access to the employees and managers. Big data access grows more awareness among the business employees. The whole hierarchy of decision-making becomes clearer by connecting big data sources (Cuzzocrea, Song and Davis, 2011). Businesses become more adept at controlling the flood of data by connecting big data sources with the entire hierarchy of decision-making. Now days, social media data has exploded and correspondingly organizations have much data to make timely decisions. Combination of big data source with other customer information sources leads to more clear reflection of current and potential customers. According to the various departments, sales and marketing resources utilises data mining approach. In the field of analytics of big data, the approach of multidimensional data plays a major role by highlighting open problems and proper research trends. The analytical contribution is accomplished at the end by generating Data Warehousing. In data warehousing, data filtration and data cleaning are two crucial steps (LaValle et al., 2011). After that we could proceed for data management. Financial and trading solutions need to be solved by predictive and trend analysis driven by big data. Big data analysis supports organizations to detect new scopes. In return, it leads to smarter business moves, efficient operations, greater profits and more satisfied customers. Big data techniques like cloud-based analytics and Hadoop bring significant advantages of cost while it comes the turn of storing huge amount of data. Additionally, management authority could detect the more effective ways of trading. The cost reduction is facilitated in this way. With the speed of Hadoop and memory analytics combined with ability to analyse new sources of data, managing authority should be able to analyse information immediately. With the ability to predict customer needs and satisfaction with the help of big data analysis, organizations are producing new products to meet requirements of customers (Chen, Chiang and Storey, 2012). High performance analysis leads to indicate fleeting scopes to get new growth scopes using information resources more efficiently. After making the data ready, software operations are applied for advanced analytics methods. Big data analysis is consists of tools for data mining, predictive analytics, forecasting, machine learning and deep learning. Trend analysis and Text mining procedures are frequently used for analytic methods. The main advantage of big data analysis is that it often involves data from both external resources and internal systems. Additionally, streaming analytics in big data analysis are becoming common in big data environments. Real-time analytics are executed by open source stream processing software such as Flink and Storm. Customer service and retail service are adopting big data analytics to meet the demands. Retail sectors are not only granting in-depth understanding but also having high quality and well-governed business questions. In customer service, data mining technology helps the company to examine large amount of data by softwares like SAS and Python and assess the outcomes. The open source software like Hadoop and Python or authorised softwares like SAS helps to compute distributing models of big data. The predictive analytics uses technological data and statistical algorithms to identify the complex features of the data. In corporate services and information technological sections, business companies generally use the technology of text mining. Emails, Twitter feeds, online surveys and blogs are the most common text based sources of big data analysed by corporate houses. Best quality of refined data provides the best interpretation. It could sort out problems regarding customer experience or complex productivity. Big data techniques analyse all the various permutations to augment that gives lesson to more quickly resolve or enhance a specific solution. IT department has the ability to measure the business outcomes for planning future services and enhancing cost measurement. Most business organization has some type of asset database that involves data. In spite of the anticipated and realised utilities of data infrastructures, big data still neither have been universally welcomed nor has been proven easy to structuralize and implement. The issues regarding big data analysis are not simply technical and human-resource based (Katal, Wazid and Goudar, 2013). Sometimes the appropriate model building becomes difficult. The amount of data included in big data analysis may cause typical data management issues in the field of data quality, consistency and governance. Besides, the data of different platforms and different conditions may provide confusing results. Integrating big data tools and software like Hadoop, Spark, SAS and Python into cohesive architectures puts the needs of big data analysis of a business organisation in a challenging position. Hence, analysts and IT team identify the right mixture of technical aspects and then put the segments of big data analysis together (Bughin, Chui and Manyika, 2010). Conclusion: Big data have strong utility and high value as they generate major outcomes to the different modes of analysis in the area of business and financing organizations. It generates innovations, policies and knowledge that shape the analysis structure. The report successfully provides a broad overview of emerging set of techniques utilised to process, analyse and implement big data analysis for business purpose especially for small financial industries. The scale and velocity of big data analysis are being more widespread and accessible. Business organisations further need to observe insights of data deluge, involving structured and unstructured data handling, basic coding, data mining, data visualization, data modelling and simulation. Data driven science would transform the underlying environmental systems. Identification of specific relationships between phenomenon and methods could create new hypotheses that might establish further studies. Big data analysis gives an effort on the businesses that are slower and less efficient with more traditional business management. Speed and efficacy are needed in big data analysis for further decision making. The better business outcomes are achieved by combination of good quality data and powerful analytics. Business organisations could be successful with the inclusion of good data and powerful analytics. Therefore, we can conclude that big data analysis for business is extraordinary relevant market-driven essential curriculum vitae. The concrete issues of management from business leaders gain exposure to the real world for successful practitioner and participants. References:- Bughin, J., Chui, M., Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch.McKinsey Quarterly,56(1), 75-86. Chen, C. P., Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347. Chen, H., Chiang, R. H., Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact.MIS quarterly,36(4). Cuzzocrea, A., Song, I. Y., Davis, K. C. (2011, October). Analytics over large-scale multidimensional data: the big data revolution!. InProceedings of the ACM 14th international workshop on Data Warehousing and OLAP(pp. 101-104). ACM. Gandomi, A., Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. Katal, A., Wazid, M., Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. InContemporary Computing (IC3), 2013 Sixth International Conference on(pp. 404-409). IEEE. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21. Liebowitz, J. (Ed.). (2013).Big data and business analytics. CRC press. McAfee, A., Brynjolfsson, E., Davenport, T. H. (2012). Big data: the management revolution.Harvard business review,90(10), 60-68. Mithas, S., Lee, M. R., Earley, S., Murugesan, S., Djavanshir, R. (2013). Leveraging Big Data and Business Analytics [Guest editors' introduction]. IT professional, 15(6), 18-20. Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40. Sagiroglu, S., Sinanc, D. (2013, May). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE. Sharda, R., Delen, D., Turban, E. (2013). Business Intelligence: A Managerial Perspective on Analytics. Prentice Hall Press. Talia, D. (2013). Clouds for scalable big data analytics. Computer, 46(5), 98-101. Waller, M. A., Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84. Zikopoulos, P., Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.

Economic Growth and Carbon Dioxide Emissions †MyAssignmenthelp

Question: Discuss about the Economic Growth and Carbon Dioxide Emissions. Answer: Introduction: Climate change is defined as the change in the global environment which occurred due to direct or indirect human practices. It is a result of increasing greenhouse gases in the environment due to human activities such as deforestation, use of non-renewable energy sources and burning of fossil fuel (Pindyck, 2013, p.865). In order to address this issue, the government can impose a carbon tax on carbon emissions of companies and people. A carbon tax is a fee that is imposed by the government on the use of fossil fuels or carbon-based fuels including gas, coal and oil (Murray and Rivers, 2015, p.678). Many countries including the United Kingdom, Sweden, Australia, Ireland and Chile has imposed a carbon tax policy (Carbon Tax, 2018, n.p). The issue is whether imposition of a carbon tax can assist in reducing carbon emissions caused by corporations and individuals that cause climate change. A carbon tax can increase the administrative costs for implementation and collection of tax which w ill slow down economic growth of countries. A carbon tax did not guarantee that carbon emissions will be reduced because companies can shift their locations to avoid carbon tax. Although the carbon tax can reduce carbon emissions by eliminating the use of fossil fuel in the manufacturing process, and it can increase the investment in renewable energy sources such as sunlight, tides, wind and biomass. However, carbon tax is not an effective option for mitigating the risk of climate change. It is difficult to increase investment in renewable energy because the administrative costs relating to managing and collecting of the carbon tax will be substantially high that will slow down the countrys economy. The government has to make high investments to ensure that every organisation pay carbon tax accordingly and it became a new burden for manufacturing firms, customers, society and the government. The manufacturing companies are more likely to increase prices for their products and services to mitigate the carbon tax expenses which increase the financial burden of the public (Conefrey et al., 2013, p.941). Furthermore, investors are less likely to invest in countries that impose a carbon tax to save capital which reduces their economy. In Australia, 93.38 percent of the energy consumed through fossil fuel which makes it difficult for the government to encourage organisations to use renewable energy sources (Trading Economics, 2018, n.p). Therefore, if a carbon tax is impose d, then prices of products and services will increase, and investment will decrease which will slow down the economic growth. However, a carbon tax can reduce carbon emissions of companies and individuals and increase the investment in renewable energy sources. Carbon tax encourages organisations to develop environment-friendly technology instead of using fossil fuels that are easily available and relatively cheaper but has a harmful impact on the environment. Due to a high rate of carbon tax, people would make efforts to find new and alternative sources of energy that are environment-friendly such as wind, solar energy, biomass, and tides (Ploeg and Withagen, 2014, p.283). For example, Australian companies are investing in wind power as a source of renewable energy which has grown 35 percent in five years up to 2011. These wind power sources generate 4,455 megawatts (MW) of energy as of 2017, and they are expected to increase up to 18,823 MW (Ramblingsdc, 2015, n.p). Therefore, if a carbon tax is imposed, then investment in renewable energy increases which address the issue of climate change. Although a carbon tax encourages investment in renewable energy, it is far from being an effective solution for climate change. Organisations are more likely to increase their products and services prices rather than investing in renewable energy sources because they require high level of investment. The government will also face difficulty in investing in renewable energy sources because the administration cost of imposing and collecting of carbon tax is substantially high (Aldy and Stavins, 2012, p.176). Investors also did not prefer to invest in countries that impose a high rate of carbon tax that would negatively affect the nations economic growth (Carl and Fedor, 2016, p.57). If carbon tax is imposed, then purchasing power and real (inflation-adjusted) salaries of people will be reduced. Therefore, carbon tax negatively affects a countrys economic growth. The government aims to reduce carbon emissions of corporations and people by imposing a carbon tax; however, it did not guarantee that global carbon emission will be reduced. One of the major contributors of greenhouse gases is manufacturing organisations; instead of paying a high rate of the carbon tax, these corporations can shift their production facilities to countries in which there is no policy of carbon tax (Martin, De Preux and Wagner, 2014, p.1). More than 53 percent of manufacturing work has been outsourced by companies to China and India because of lower labour costs and lack of carbon tax (Statistic Brain, 2017, n.p). Therefore, if a harmonised carbon tax system is not applied worldwide, then it cannot reduce carbon emissions of large companies since they can switch their production location which reduces the impact of a carbon tax system. A carbon tax reduces the negative impact of climate change by reducing huge amount of carbon emissions caused by manufacturing companies and individuals that increase greenhouse gases in the environment. If the government did not make appropriate efforts to reduce carbon emissions, then, it will damage the environment and its resources. In order to preserve the environment, the level of carbon dioxide emission is required to decrease that can be achieved by the imposition of a carbon tax (Samimi and Zarinabadi, 2012, p.1012). In Sweden, from 2000 to 2012, greenhouse gas emissions reduced by 16 percent (OCED, 2014, p.4). Between 1990 and 2005, Demark reported a reduction in carbon emissions per person by 15 percent (Nunez, 2018, n.p). As a result, if carbon tax is imposed, then environmental conditions can be improved. However, carbon tax cannot completely mitigate the risk of climate change because it did not implement across the world. It is not a suitable option due to lack of a globally harmonised carbon tax system. Countries such as Norway and Finland are able to effectively implement carbon tax because of stable economic conditions and low population in the country (Di Cosmo and Hyland, 2013, p.409). However, in large nations such as China, India, EU and USA, it is difficult for governments to impose and collect tax from all organisations effectively (Li et al., 2013, p.927). Countries such as China and India are heavily populated, and they make about 37 percent of worlds total carbon emissions (China 30 percent and India 7 percent) (United States Environmental Protection Agency, 2017, n.p). If not efforts made by these two nations for reducing carbon emissions, then it is difficult to reduce carbon emissions around the world by imposing carbon tax. Therefore, carbon tax is not a suitable opt ion. In conclusion, a carbon tax policy can reduce carbon emissions of organisations and individuals, and it can increase the investment in green energy sources which can address the issue of climate change. However, governments face many financial and economic difficulties while implementing a carbon tax policy. It increases the financial burden on companies, people and government. Moreover, corporations can switch their production location to avoid carbon tax due to lack of harmonised carbon tax system worldwide. Therefore, carbon tax is not the best solution for addressing the issue of climate change. Thus, governments should seek other policies for addressing the issue of climate change such as eco-friendly transportation system or recycling and companies across the world should participate in such programs worldwide. References Aldy, JE and Stavins, RN 2012, The promise and problems of pricing carbon: Theory and experience,The Journal of Environment Development, vol. 21, no. 2, pp. 152-180. Carbon Tax 2018, Where Carbon Is Taxed, Carbon Tax, viewed 31 March 2018, https://www.carbontax.org/where-carbon-is-taxed/ Carl, J and Fedor, D 2016, Tracking global carbon revenues: A survey of carbon taxes versus cap-and-trade in the real world,Energy Policy, vol. 96, pp. 50-77. Conefrey, T, Fitz Gerald, JD, Valeri, LM and Tol, RS 2013, The impact of a carbon tax on economic growth and carbon dioxide emissions in Ireland,Journal of Environmental Planning and Management, vol. 56, no. 7, pp. 934-952. Di Cosmo, V and Hyland, M 2013, Carbon tax scenarios and their effects on the Irish energy sector,Energy Policy, vol. 59, pp. 404-414. Li, A, Zhang, A, Cai, H, Li, X and Peng, S 2013, How large are the impacts of carbon-motivated border tax adjustments on China and how to mitigate them?,Energy Policy, vol. 63, pp. 927-934. Martin, R, De Preux, LB and Wagner, UJ 2014, The impact of a carbon tax on manufacturing: Evidence from microdata,Journal of Public Economics, vol.117, pp. 1-14. Murray, B and Rivers, N 2015, British Columbias revenue-neutral carbon tax: A review of the latest grand experiment in environmental policy,Energy Policy, vol. 86, pp. 674-683. Nunez, C 2018, Whats A Carbon Tax, And How Does It Reduce Emissions?, National Geographic, viewed 6 April 2018, https://channel.nationalgeographic.com/before-the-flood/articles/whats-a-carbon-tax-and-how-does-it-reduce-emissions/ OCED 2014, Environmental Performance Reviews: Sweden, OCED, viewed 6 April 2018, https://www.oecd.org/environment/country-reviews/Sweden%20Highlights%20web%20pages2.pdf Pindyck, RS 2013, Climate change policy: What do the models tell us?,Journal of Economic Literature, vol. 51, no. 3, pp. 860-72. Ploeg, F and Withagen, C 2014, Growth, renewables, and the optimal carbon tax,International Economic Review,vol. 55, no. 1, p. 283. Ramblingsdc 2015 Wind power and wind farms in Australia, Ramblingsdc viewed 6 April 2018, https://www.ramblingsdc.net/Australia/WindPower.html Samimi, A and Zarinabadi, S 2012, Reduction of greenhouse gases emission and effect on environment,Journal of American Science,vol. 8, no. 8, pp. 1011-1015. Statistic Brain 2017, Job Overseas Outsourcing Statistics, Statistic Brain viewed 6 April 2018, https://www.statisticbrain.com/outsourcing-statistics-by-country/ Trading Economics 2018, Australia Fossil fuel energy consumption (% of total), Trading Economics, viewed 6 April 2018, https://tradingeconomics.com/australia/fossil-fuel-energy-consumption-percent-of-total-wb-data.html United States Environmental Protection Agency 2017, Global Greenhouse Gas Emissions Data, EPA, viewed 6 April 2018, https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data