15 research outputs found

    The role of RFID in agriculture: Applications, limitations and challenges

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    The recent advances in RFID offer vast opportunities for research, development and innovation in agriculture. The aim of this paper is to give readers a comprehensive view of current applications and new possibilities, but also explain the limitations and challenges of this technology. RFID has been used for years in animal identification and tracking, being a common practice in many farms. Also it has been used in the food chain for traceability control. The implementation of sensors in tags, make possible to monitor the cold chain of perishable food products and the development of new applications in fields like environmental monitoring, irrigation, specialty crops and farm machinery. However, it is not all advantages. There are also challenges and limitations that should be faced in the next years. The operation in harsh environments, with dirt, extreme temperatures; the huge volume of data that are difficult to manage; the need of longer reading ranges, due to the reduction of signal strength due to propagation in crop canopy; the behavior of the different frequencies, understanding what is the right one for each application; the diversity of the standards and the level of granularity are some of them

    Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

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    Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (โ‰ˆ50%) still occur during the packaging, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables

    Blockchain Technology for Enhancing Supply Chain Performance and Reducing the Threats Arising from the COVID-19 Pandemic

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    A rigorous examination of the most recent advancements in blockchain technology (BCT) and artificial intelligence (AI)-enabled supply chain networks is provided in this book. The edited book brings together the perspectives of a number of authors who have presented their most recent views on blockchain technology and its applications in a variety of disciplines. The submitted papers contribute to a better understanding of how blockchain technology can improve the efficacy of human activities during a pandemic, improve traceability and visibility in the automotive supply chain, support food safety and reliability through digitalisation of the food supply chain, and increase the performance of next-generation digital supply chains, among other things. The book attempts to address and prepare a way to address the complicated issues that supply chains are encountering as a result of the global pandemic

    Blockchain-Based Digitalization of Logistics Processesโ€”Innovation, Applications, Best Practices

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    Blockchain technology is becoming one of the most powerful future technologies in supporting logistics processes and applications. It has the potential to destroy and reorganize traditional logistics structures. Both researchers and practitioners all over the world continuously report on novel blockchain-based projects, possibilities, and innovative solutions with better logistic service levels and lower costs. The idea of this Special Issue is to provide an overview of the status quo in research and possibilities to effectively implement blockchain-based solutions in business practice. This Special Issue reprint contained well-prepared research reports regarding recent advances in blockchain technology around logistics processes to provide insights into realized maturity

    ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ RFID ๋„์ž… ์š”์ธ๊ณผ ํšจ๊ณผ์„ฑ ๋ถ„์„ -์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ํ†ตํ•œ ์ •์ฑ… ํ™œ์„ฑํ™”์˜ ๊ด€์ ์—์„œ-

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ–‰์ •ํ•™๊ณผ, 2017. 2. ์ •๊ด‘ํ˜ธ.๊ตญ๋ฌธ์ดˆ๋ก ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์ง€๋‚œ 2013๋…„ ๋„์ž…๋˜๊ธฐ ์‹œ์ž‘ํ•œ RFID ๊ฐœ๋ณ„ ๊ณ„๋Ÿ‰ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ ๋ฐฉ์‹์˜ ๋„์ž… ์š”์ธ์„ ๋ฐํžˆ๊ณ  ์ •์ฑ… ํšจ๊ณผ ๋ฅผ ์‹ค์ฆํ•จ์œผ๋กœ์จ ์ •์ฑ… ํ™œ์„ฑํ™”๋ฅผ ์œ„ํ•œ ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•˜๋Š” ๋ฐ ์žˆ๋‹ค. ์šฐ ๋ฆฌ๋‚˜๋ผ ํ™˜๊ฒฝ๋ถ€๋Š” ์ง€๋‚œ 2013๋…„ ์ดํ›„ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ๋ฅผ ๋„์ž…ํ•œ ์ด๋ž˜ ๋ฐฐ์ถœ๋Ÿ‰์„ ๊ฐ๋Ÿ‰ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ ์ธ์ƒ์ด๋ผ๋Š” ์ •์ฑ… ์ˆ˜ ๋‹จ(๋„๊ตฌ)๋ฅผ ํ™œ์šฉํ•ด์™”๋‹ค. ์ดํ›„ ์“ฐ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰ ํšจ๊ณผ๋ฅผ ๋”์šฑ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ์— ๊ธฐ๋ฐ˜ํ•œ RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋ฐฉ์‹์˜ ๋„์ž…์„ ์ถ” ์ง„ํ•˜๊ณ  ์„ค์น˜์— ํ•„์š”ํ•œ ๋ชจ๋“  ๋น„์šฉ์„ ๊ตญ๊ณ ๋กœ ๋ณด์กฐํ•ด์™”๋‹ค(์„œ์šธ์‹œ, 2016). ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ํŒŒ๊ฒฉ์ ์ธ ์ง€์›์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  2๋…„์ด ์ง€๋‚œ 2015 ๋…„๊นŒ์ง€ ์„œ์šธ์‹œ๋‚ด ์•„ํŒŒํŠธ ๋‹จ์ง€์˜ ์•ฝ 20%์—์„œ๋งŒ RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋ฐฉ์‹ ์„ ๋„์ž…ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ ์ž์น˜๊ตฌ์—์„œ๋Š” ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰์„ ์œ„ํ•˜์—ฌ ์„ธ ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ์˜ ๋„์ž…๋ณด๋‹ค๋Š” ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ๋ฅผ ์ธ์ƒํ•˜๋Š” ๊ฒƒ์„ ๋” ์„ ํ˜ธํ•˜ ๊ณ  ์žˆ๋‹ค. ๊ทธ๋ ‡๋‹ค๋ฉด ์™œ RFID ๋ฐฉ์‹์˜ ๋„์ž…๊ณผ ํ™œ์šฉ์ด ์›ํ™œํ•˜๊ฒŒ ์ด๋ฃจ์–ด ์ง€์ง€ ์•Š๊ณ  ์žˆ๋Š” ๊ฒƒ์ผ๊นŒ? ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์˜์‹ ํ•˜์— ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์„ธ ๊ฐ€์ง€์ด๋‹ค. ์ฒซ์งธ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„œ์šธ์‹œ๋‚ด ์•„ํŒŒํŠธ ๋‹จ์ง€ 2081๊ฐœ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„(logistic analysis)์„ ์‹ค์‹œํ•˜์—ฌ ์–ด๋– ํ•œ ์š”์ธ์ด RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋„์ž…์„ ์ขŒ์šฐํ•˜์˜€๋Š”์ง€ ์กฐ์ง์ (์•„ํŒŒํŠธ ๋‹จ์ง€) ์ˆ˜์ค€์—์„œ ๋ถ„์„ํ•˜์—ฌ ์•„ํŒŒํŠธ ๋‹จ์ง€์˜ RFID ๋„์ž…์„ ์ขŒ์šฐํ•˜๋Š” ์š”์ธ์ด ๋ฌด ์—‡์ธ์ง€ ์‚ดํŽด๋ณผ ๊ฒƒ์ด๋‹ค. ๋‘ ๋ฒˆ์งธ, ๋งˆํฌ๊ตฌ์—์„œ ์ˆ˜์ง‘ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ”ํƒ• ์œผ๋กœ PSM-DID ๋ถ„์„์„ ์‹ค์‹œํ•˜์—ฌ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ์˜ ์“ฐ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰ ํšจ ๊ณผ๋ฅผ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋ฐฉ์‹์—์„œ๋งŒ ๊ฐ€๋Šฅํ•œ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ๊ฐ€ ํšจ๊ณผ์ ์ธ ์ •์ฑ…์ˆ˜๋‹จ์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  RFID ๋ฐฉ์‹์˜ ๋„์ž…์ด ์›ํ™œํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š๋Š” ์ด์œ ๋ฅผ ์ •์ฑ… ์ˆ˜๋‹จ์  ๊ด€์  ์—์„œ ์‚ดํŽด๋ณด์•˜๋‹ค. ์„ธ ๋ฒˆ์งธ, ์„ฑ๋ถ๊ตฌ์˜ ์‚ฌ๋ก€๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹œ๊ณ„์—ด ๋ถ„์„ ์„ ์‹ค์‹œํ•˜์—ฌ ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ ์ธ์ƒ์ด ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰์— ๋ฏธ์น˜๋Š” ํšจ ๊ณผ๊ฐ€ ๋ฏธ๋ฏธํ•จ์„ ์‹ค์ฆํ•˜์˜€๋‹ค. ๊ถ๊ทน์ ์œผ๋กœ ์„ธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•˜์—ฌ RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋ฐฉ์‹์˜ ๋„์ž…๊ณผ ํ™œ์šฉ์„ ํ™œ์„ฑํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ์ •์ฑ…์  ์ œ ์–ธ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์€ Ascher(1987), Brunner(1996), deLeon(1994) ๋“ฑ๊ณผ ๊ฐ™์€ ์ •์ฑ…ํ•™์ž๋“ค์ด ์ •์ฑ…ํ•™์˜ ์‹คํŒจ์›์ธ์œผ๋กœ ์ง€์  ํ•˜๊ณ  ์žˆ๋Š” ์„ธ ๊ฐ€์ง€ ์š”์ธ์—์„œ๋ถ€ํ„ฐ ์ฐพ์•„๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์ด๋“ค์— ์˜ํ•˜๋ฉด ์ • ์ฑ…ํ•™์˜ ์‹คํŒจ ์›์ธ์€ (1) ๊ธฐ์ˆ ๊ด€๋ฃŒ์  ์ง€ํ–ฅ์„ฑ(technocratic orientation) ์œผ๋กœ ์ธํ•œ ์ •์น˜/๊ด€๊ณ„์˜ ๋ฐฐ์ œ, (2) ๋ถ„์„์  ์˜ค๋ฅ˜(analytical error)๋กœ ์ธํ•œ ๋งฅ๋ฝ์˜ ์™œ๊ณก๊ณผ ๊ฐ„๊ณผ, (3) ๋„๊ตฌ์  ํ•ฉ๋ฆฌ์„ฑ(intstrumental rationality)์˜ ์ง€๋‚˜์นœ ์ถ”๊ตฌ์˜ ์„ธ ๊ฐ€์ง€๋กœ ์ •๋ฆฌํ•˜์—ฌ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์— ๊ด€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์ด RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋ฐฉ์‹ ๋„์ž…๊ณผ ํ™œ์šฉ ์ง€ ์—ฐ์— ๋Œ€ํ•œ ํ•ด๋‹ต์„ ์ œ์‹œํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋Š” ์ด์œ ๋Š”, ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์ด ์ • ์ฑ…ํ•™์˜ ์„ธ ๊ฐ€์ง€ ์‹คํŒจ์›์ธ์„ ๋‚ดํฌํ•˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ฒซ ๋ฒˆ์งธ, ์“ฐ๋ ˆ ๊ธฐ ์ข…๋Ÿ‰์ œ ๋„์ž…์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ๋“ค์€ ์ข…๋Ÿ‰์ œ ๋„์ž… ์š”์ธ์„ ๋„์ถœํ•˜๋Š” ๋ฐ ๊ฑฐ์˜ ๊ด€์‹ฌ์„ ๊ฐ€์ง€์ง€ ์•Š์•˜์œผ๋ฉฐ, ํŠนํžˆ ๋„์ž… ๊ณผ์ •์—์„œ ์ง€์—ญ์ฃผ๋ฏผ๋“ค๊ณผ ์ •๋ถ€ ๊ฐ„ ๊ด€๊ณ„์— ์ฃผ๋ชฉํ•˜์ง€ ์•Š์•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ RFID ๋ฐฉ์‹์˜ ๋„์ž…์„ ๊ฒฐ์ • ํ•˜๋Š” ๊ฐ€์žฅ ์‹ค์งˆ์  ๋‹จ์œ„๋Š” ์•„ํŒŒํŠธ ๋‹จ์ง€์ด๋ฉฐ RFID ๋ฐฉ์‹์€ ์•„ํŒŒํŠธ ๋‹จ ์ง€ ์ฃผ๋ฏผ๋“ค์ด ์ž์น˜์ ์œผ๋กœ ๊ฒฐ์ •ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ธฐ์ˆ ๊ด€๋ฃŒ์  ๊ด€์ ์—์„œ ๋ฒ— ์–ด๋‚˜ ์•„ํŒŒํŠธ ๋‹จ์ง€์˜ ํŠน์„ฑ ๋ฐ ์ •๋ถ€์™€ ์•„ํŒŒํŠธ ๋‹จ์ง€ ์ฃผ๋ฏผ๋“ค์˜ ์ƒํ˜ธ์ž‘ ์šฉ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋„์ž… ์š”์ธ์„ ๋„์ถœํ•˜์˜€์„ ๋•Œ, ๋น„๋กœ์†Œ RFID ๋ฐฉ์‹ ๋„์ž… ์— ๊ด€ํ•œ ๊ฐ€์žฅ ์ ์‹ค์„ฑ ์žˆ๋Š” ์ •์ฑ…์  ์ œ์–ธ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์„œ์šธ ์‹œ ์•„ํŒŒํŠธ ๋‹จ์ง€๋ฅผ ๋ถ„์„ ๋‹จ์œ„๋กœ ๋„์ž…์š”์ธ์„ ๋„์ถœํ•œ ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ํ–ฅํ›„ ์ •์ฑ… ๋„์ž… ํ™œ์„ฑํ™”๋ฅผ ์œ„ํ•œ ํƒ€๊ฒŸํŒ…๊ณผ ํ–ฅํ›„ ์ •์ฑ… ํ™œ์„ฑํ™”๋ฅผ ์œ„ ํ•˜์—ฌ ์ •๋ถ€๊ฐ€ ํ•ด์•ผ ํ•˜๋Š” ์—ญํ• ์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ, ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์˜ ํšจ๊ณผ๋ฅผ ๋ฐํžŒ ์—ฐ๊ตฌ๋“ค์—๋Š” ๋ช‡ ๊ฐ€์ง€ ๋ถ„์„์ƒ์˜ ์˜ค๋ฅ˜๋“ค์ด ์กด์žฌ ํ•˜์—ฌ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ์˜ ํšจ๊ณผ๋ฅผ ๋ฉด๋ฐ€ํžˆ ๋ฐํžˆ์ง€ ๋ชปํ•˜์˜€๋‹ค. RFID ๊ฐœ๋ณ„ ๊ณ„๋Ÿ‰ ๋ฐฉ์‹์˜ ๋„์ž… ์ „์—๋Š” ์ธก์ • ๊ธฐ์ˆ ์˜ ํ•œ๊ณ„๋กœ ์ธํ•˜์—ฌ ์„ธ๋Œ€๋ณ„๋กœ ๋ฐฐ ์ถœํ•œ ์“ฐ๋ ˆ๊ธฐ๋Ÿ‰์„ ์ธก์ • ๋ฐ ์ˆ˜์ง‘ํ•  ์ˆ˜ ์—†์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ๋ถ„์„ ๋‹จ์œ„๊ฐ€ ๊ด‘ ์—ญ์‹œ/๋„๋กœ ๋งค์šฐ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์„ค์ •๋˜๊ฑฐ๋‚˜(ํ™์„ฑํ›ˆ, 2001) ๊ฐ ์„ธ๋Œ€์—๊ฒŒ ๋ฐฐ์ถœ๋Ÿ‰์„ ์ง์ ‘ ๋ฆฌํฌํŠธํ•˜๊ฒŒ ํ•˜๋Š” ๋ฐฉ์‹(Houtven and Morris, 1999)์œผ ๋กœ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋งˆํฌ๊ตฌ์— ์œ„์น˜ํ•œ 73๊ฐœ ์•„ํŒŒํŠธ ๋‹จ์ง€๋“ค์„ ๋Œ€์ƒ์œผ๋กœ DID ๋ถ„์„์„ ์‹ค์‹œํ•˜์—ฌ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ ๋ฐฉ์‹์˜ ์“ฐ ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰ ํšจ๊ณผ๋ฅผ ๋ฐํ˜”๋‹ค. ์„ธ ๋ฒˆ์งธ, ์ง€๋‚˜์นœ ๋„๊ตฌ์  ํ•ฉ๋ฆฌ์„ฑ์˜ ๊ฐ•์กฐ๋กœ ์ธํ•˜์—ฌ ์ •์ฑ… ์ˆ˜๋‹จ์  ๊ด€์ ์—์„œ์˜ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์˜ ๊ด€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ข…๋Ÿ‰์ œ ์ด์ „์˜ ์“ฐ๋ ˆ๊ธฐ ์ˆ˜๊ฑฐ ๋ฐฉ์‹๊ณผ ๋น„๊ตํ•˜์—ฌ ์ข…๋Ÿ‰์ œ๊ฐ€ ์–ผ๋งˆ๋‚˜ ์“ฐ๋ ˆ๊ธฐ๋ฅผ ๊ฐ๋Ÿ‰ํ–ˆ๋Š”์ง€์— ์ฃผ๋กœ ๊ด€์‹ฌ์„ ๊ธฐ ์šธ์—ฌ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ข…๋Ÿ‰์ œ๋ฅผ ๋„๊ตฌ์  ๊ด€์ ์—์„œ๋งŒ ํŒŒ์•…ํ–ˆ์„ ๋•Œ์—๋Š” ์ • ์ฑ… ์ˆ˜๋‹จ์ด ํšจ๊ณผ์ ์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์™œ ๋„์ž…๋˜์ง€ ์•Š๋Š”์ง€, ํ˜น์€ ๋„์ž…์„ ๋”์šฑ ํ™œ์„ฑํ™”ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์–ด๋– ํ•œ ์š”๊ฑด์„ ๊ฐ–์ถ”์–ด์•ผํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ๋‹จ์ดˆ๋ฅผ ์ œ์‹œํ•  ์ˆ˜ ์—†๋‹ค. ๋”ฐ๋ผ์„œ ๋ณด๋‹ค ๊ฒฝ์ œ์  ํšจ์œจ์„ฑ ์™ธ์— ๋‹ค์–‘ํ•œ ๊ฐ€ ์น˜๋“ค์„ ๋ชจ๋‘ ํฌ๊ด„ํ•˜๋Š” ํ˜•ํƒœ๋กœ ์ •์ฑ… ํ‰๊ฐ€๊ฐ€ ์ด๋ฃจ์–ด์ ธ์•ผํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๋Š” ๋„๊ตฌ์  ํ•ฉ๋ฆฌ์„ฑ์—์„œ ๋ฒ—์–ด๋‚˜ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ์™€ ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ ์ธ์ƒ์„ ์ •์ฑ… ์ˆ˜๋‹จ์  ๊ด€์ ์—์„œ ํŒŒ์•…ํ•จ์œผ๋กœ์จ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ๊ฐ€ ํšจ๊ณผ์ ์œผ๋กœ ์ •์ฑ… ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ •์ฑ…์ˆ˜๋‹จ์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋„์ž…์ด ์ง€์—ฐ ๋˜๊ณ  ์žˆ๋Š” ์ด์œ ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ€์ง€๋Š” ์ด๋ก ์  ํ•จ์˜๋ฅผ ์‚ดํŽด๋ณด๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋จผ ์ € ์ „ํ†ต์  ๊ฒฝ์ œํ•™์—์„œ ๋…ผ๋ž€์ด ๋˜์–ด์™”๋˜ ๊ทธ๋ฃน ์ธ์„ผํ‹ฐ๋ธŒ์™€ ๊ฐœ์ธ ์ธ์„ผํ‹ฐ๋ธŒ์˜ ํšจ๊ณผ๋ฅผ ๋น„๊ตํ•˜์—ฌ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฐœ์ธ ์ธ์„ผํ‹ฐ๋ธŒ ๋ฐฉ์‹์˜ ํšจ๊ณผ์„ฑ ์„ ์ฃผ์žฅํ•˜๋Š” ํ•™์ž๋“ค์€ ๊ทธ๋ฃน ์ธ์„ผํ‹ฐ๋ธŒ ๋ฐฉ์‹์ด ์•ผ๊ธฐํ•˜๋Š” ์‚ฌํšŒ์  ํƒœ๋งŒ (social loafing)๊ณผ ๋ฌด์ž„์Šน์ฐจ(free riding) ํ˜„์ƒ์„ ์ง€์ ํ•œ๋‹ค. ๋งˆํฌ๊ตฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ DID ๋ถ„์„ ๊ฒฐ๊ณผ ๋‹จ์ง€๋ณ„ ์ข…๋Ÿ‰์ œ ๋ฐฉ์‹๋ณด๋‹ค RFID๋ฅผ ํ™œ์šฉ ํ•œ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ ๋ฐฉ์‹์—์„œ ๋ณด๋‹ค ํฐ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰ ํšจ๊ณผ๊ฐ€ ์žˆ ๋Š” ๊ฒƒ์œผ๋กœ ๋„์ถœ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๊ฐœ์ธ ์ธ์„ผํ‹ฐ๋ธŒ ์ œ๋„๊ฐ€ ํšจ๊ณผ์  ์œผ๋กœ ์กฐ์ง์˜ ์„ฑ๊ณผ๋ฅผ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ์Œ์„ ์‹ค์ฆ์ ์œผ๋กœ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ธ์„ผํ‹ฐ๋ธŒ ์ฒด๊ณ„์˜ ์ž‘๋™์— ์žˆ์–ด ์„ฑ๊ณผ์˜ ์ธก์ •๊ณผ ํ‰๊ฐ€ ๊ฐ€ ๊ฐ€์ง€๋Š” ์ค‘์š”์„ฑ์„ ์žฌํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ์—ฐ๊ตฌํ•œ ํ•™์ž๋“ค ์€ ์„ฑ๊ณผ์˜ ๊ณต์ •ํ•œ ์ธก์ •์ด ์ธ์„ผํ‹ฐ๋ธŒ ์ œ๋„์˜ ํšจ๊ณผ์  ๊ตฌ์ถ•๊ณผ ์ž‘๋™์— ์˜ํ–ฅ์„ ๋ฏธ์น ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๋” ๋‚˜์•„๊ฐ€ ์กฐ์ง ๋‚ด ์‹ ๋ขฐ์™€ ์กฐ์ง ๊ตฌ์„ฑ์›์˜ ํƒœ์—…, ์กฐ์ง ์ดํƒˆ์—๊นŒ์ง€ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์ค‘์š”ํ•œ ์š”์†Œ์ž„์„ ๊ฐ•์กฐ ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์˜ค์—ผ์„ธ(emission fee)์˜ ์˜ค์—ผ ๋ฐฐ์ถœ๋Ÿ‰ ๊ฐ์†Œ ํšจ๊ณผ์— ๋Œ€ํ•˜์—ฌ ์‹ค์ฆํ•˜์—ฌ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์˜ค์—ผ์„ธ๋Š” ์‹œ์žฅ ์›๋ฆฌ๋ฅผ ์ž‘๋™์‹œ์ผœ ํ™˜๊ฒฝ ์˜ค์—ผ์„ ์ค„์ด๊ณ ์ž ๊ณ ์•ˆ๋œ ์ •์ฑ… ์ˆ˜๋‹จ ์ค‘ ํ•˜๋‚˜๋กœ, ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ๋ฅผ ๋Œ€ ์ƒ์œผ๋กœ ํ•œ ์—ฐ๊ตฌ์—์„œ๋„ ๊ทธ ํšจ๊ณผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ด๋ฃจ์–ด์ ธ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์“ฐ๋ ˆ๊ธฐ ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ์˜ ์ธ์ƒ์ด ์“ฐ๋ ˆ๊ธฐ ๋ฐฐ์ถœ๋Ÿ‰์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ์ง€์— ๋Œ€ํ•œ ์ƒ๋ฐ˜๋œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๊ฐ€ ์กด์žฌํ•˜๋Š” ์ƒํƒœ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ํ˜„์žฌ ์šฐ๋ฆฌ๋‚˜๋ผ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์—์„œ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋Š” ์“ฐ๋ ˆ๊ธฐ ๋ฐฐ ์ถœ ์ˆ˜์ˆ˜๋ฃŒ์˜ ์˜ค์—ผ ๊ฐ์†Œ ํšจ๊ณผ๊ฐ€ ๊ฑฐ์˜ ์—†์Œ์„ ์‹ค์ฆํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ€์ง€๋Š” ์ •์ฑ…์  ํ•จ์˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋จผ์ € ์ธ์„ผํ‹ฐ ๋ธŒ ์‹œ์Šคํ…œ์˜ ํšจ๊ณผ์  ์ž‘๋™์„ ์œ„ํ•˜์—ฌ ํ˜์‹  ๊ณผํ•™๊ธฐ์ˆ ์ด ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜๋Š” ์—ญํ• ์„ ์žฌ์ ๊ฒ€ํ•˜์˜€๋‹ค. ์ •์ฑ…์˜ ์˜์—ญ์—์„œ ๊ณผํ•™๊ธฐ์ˆ ์ด ์ˆ˜ํ–‰ํ•ด์•ผํ•˜๋Š” ๋ฐ” ๋Š” ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋‹ค๋ฃฌ ์‚ฌ๋ก€์—์„œ์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๊ธฐ์กด ์ œ๋„์˜ ํ‹€ ์•ˆ์— ์„œ ์ œ๋„์˜ ํšจ์œจ์  ์ˆ˜ํ–‰์„ ๊พ€ํ•  ์ˆ˜ ์žˆ๋Š” ๋„๊ตฌ์  ์—ญํ• ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ๊ณผํ•™๊ธฐ์ˆ ์ด ์ธ๊ฐ„์˜ ์„ฑ๊ณผ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์ธก์ •ํ•˜๊ณ  ์ •๋ณด์˜ ์ถ•์ ์„ ์šฉ์ดํ•˜๊ฒŒ ํ•˜์—ฌ ์ธ๊ฐ„์ด ๊ตฌ์ถ•ํ•œ ์ธ์„ผํ‹ฐ๋ธŒ ์‹œ์Šคํ…œ์˜ ํšจ ๊ณผ์  ์ž‘๋™์„ ๊พ€ํ•œ ์‚ฌ๋ก€๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ •์ฑ… ์ˆ˜๋‹จ์˜ ํ•œ ๋ฐฉ๋ฒ•์œผ ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๊ทธ๋ฃน ์ธ์„ผํ‹ฐ๋ธŒ์™€ ๊ฐœ์ธ ์ธ์„ผํ‹ฐ๋ธŒ ์ค‘ ๊ฐœ์ธ ์ธ์„ผํ‹ฐ ๋ธŒ ๋ฐฉ์‹์ด ๊ฐ€์ง€๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์—ฌ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ง‘๋‹จ ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ์—ฐ๊ตฌํ•œ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ํ•™์ž๋“ค์€ ํ•œ๊ตญ๊ณผ ๊ฐ™์ด ์ง‘๋‹จ์ฃผ์˜ ๋ฌธํ™”๊ฐ€ ๊ฐ•ํ•˜๊ฒŒ ์ž‘์šฉํ•˜๋Š” ์ง‘๋‹จ์—์„œ ๊ฐœ์ธ ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ๋„์ž…ํ•˜๋Š” ๊ฒƒ์€ ์ƒํ˜ธ๊ฐ„์˜ ํ˜‘๋ ฅ ์„ ์ €ํ•ดํ•˜๊ณ  ๋ถˆํ•„์š”ํ•œ ๊ฒฝ์Ÿ์„ ์•ผ๊ธฐํ•  ๊ฒƒ์ž„์„ ๊ฒฝ๊ณ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ํ•œ๊ตญ์  ์ƒํ™ฉ ํ•˜์—์„œ๋„ ์ถฉ๋ถ„ํžˆ ๊ฐœ์ธ ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ํšจ๊ณผ ์  ์ •์ฑ… ์ˆ˜๋‹จ์œผ๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰์— ์žˆ์–ด RFID ๋ฐฉ์‹ ๋„์ž…์ด ๋ฏธ์น˜๋Š” ๊ธ์ •์  ํšจ๊ณผ๋ฅผ ์‹ค์ฆํ•จ์œผ๋กœ์จ, ์ •์ฑ… ๋„์ž…์˜ ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•˜์˜€ ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ฒ˜๋ฆฌ์— ์žˆ์–ด RFID ๊ธฐ์ˆ ์ด ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์‹ค์ฆ๋˜์–ด์•ผ ํ•˜๋Š” ๊ทผ๊ฑฐ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์„ค๋ช…ํ•˜์—ฌ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋จผ์ €, ํ™˜๊ฒฝ๋ถ€(2013)์—์„œ ๋ฐํžˆ๊ณ  ์žˆ๋Š” ๋ฐ”์™€ ๊ฐ™์ด ํ™˜๊ฒฝ๋ถ€๋Š” ์Œ์‹๋ฌผ ์“ฐ ๋ ˆ๊ธฐ ๊ฐ๋Ÿ‰์„ ์œ„ํ•˜์—ฌ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ์— ๊ธฐ๋ฐ˜ํ•œ RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋ฐฉ์‹์„ ๋งค์šฐ ์ •๋ ฅ์ ์œผ๋กœ ๋„์ž…ํ•˜๊ณ ์ž ํ•˜๋‚˜, ์ž์น˜๊ตฌ์—์„œ๋Š” ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ์˜ ์ธ ์ƒ์„ ํ†ตํ•˜์—ฌ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ๋ฅผ ๊ฐ๋Ÿ‰ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ ํ•˜์—์„œ ์Œ์‹๋ฌผ ๊ฐ๋Ÿ‰์— ๋Œ€ํ•œ RFID ๋ฐฉ์‹์˜ ํšจ๊ณผ๋ฅผ ์‹ค์ฆํ•œ ๊ฒƒ์€ ํ–ฅํ›„ RFID ๊ธฐ์ˆ ์˜ ์ ๊ทน์  ๋„์ž…์„ ๋’ท๋ฐ›์นจํ•ด์ค„ ์ˆ˜ ์žˆ๋Š” ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•ด์ฃผ์—ˆ๋‹ค๊ณ  ํ•  ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๋จผ์ € ๋„์ž… ์š” ์ธ ๋ถ„์„์— ์žˆ์–ด ๋„์ž…์ด ์ด๋ฃจ์–ด์ง€๊ธฐ๊นŒ์ง€ ์–ผ๋งˆ๋งŒํผ์˜ ์‹œ๊ฐ„์ด ๊ฑธ๋ ธ๋Š” ์ง€, ๊ทธ๋ฆฌ๊ณ  ํ˜„์žฌ ์ „์ฒด ํ‘œ๋ณธ ์ค‘ RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰์ด ๋„์ž…๋œ ํ‘œ๋ณธ์€ ์–ด ๋Š ์ •๋„์ธ์ง€์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ข…์†๋ณ€์ˆ˜์— ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ์ง€ ๋ชปํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰์˜ ๋„์ž…์ด ์–ด๋Š ์ •๋„ ์™„๋ฃŒ๋œ ์‹œ์ ์—์„œ๋Š” ์ƒ ์กด๋ถ„์„(survival analysis)๋ฒ•, ๊ทธ ์ค‘์—์„œ๋„ ์œ„ํ—˜ํ•จ์ˆ˜(hazard function) ์„ ์ด์šฉํ•œ ๋ถ„์„์ด ์ด๋ฃจ์–ด์ ธ์•ผํ•  ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์˜ ์—ฐ๊ตฌ๋Š” ์„œ ์šธ์‹œ 25๊ฐœ ์ž์น˜๊ตฌ ์ค‘ 2๊ฐœ ๊ตฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ, ์ œํ•œ์ ์ธ ์‹œ๊ฐ„์  ๋ฒ”์œ„ ๋‚ด ์—์„œ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค๋Š” ํ•œ๊ณ„์ ์„ ์ง€๋‹ˆ๋ฉฐ ์ด๋ฅผ ํ† ๋Œ€๋กœ ์Œ์‹๋ฌผ ์“ฐ ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์˜ ํšจ๊ณผ๋ฅผ ์ผ๋ฐ˜ํ™”ํ•˜๋Š” ๊ฒƒ์ด ์˜ณ์€์ง€์— ๋Œ€ํ•œ ๋น„ํŒ์— ์ง๋ฉด ํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ์™€ ๋‹จ์ง€๋ณ„ ์ข… ๋Ÿ‰์ œ์˜ DID ๋ถ„์„์—์„œ ๊ฒฝํ–ฅ์ ์ˆ˜๋งค์นญ(Propensity Score Matching)๋ฒ• ์„ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜ ์ง‘๋‹จ๊ณผ ํ†ต์ œ ์ง‘๋‹จ์˜ 1๋Œ€ 1 ๋งค์นญ์„ ์‹œ๋„ํ•œ ํ›„ ์ด ์ค‘ํ†ต์ œ(Doubly Robust Estimation)๋ฅผ ํ™œ์šฉํ•œ DID๋ถ„์„์„ ์‹œ๋„ํ•˜์˜€ ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฒฝํ–ฅ์ ์ˆ˜ ๋งค์นญ์˜ ํšจ๊ณผ์„ฑ์— ๋Œ€ํ•ด์„œ๋Š” ๋งŽ์€ ๋น„ํŒ๋“ค์ด ์กด์žฌ ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ฐ์ดํ„ฐ์˜ ๋ถ„์„๋‹จ์œ„ ์˜ค๋ฅ˜๋ฅผ ์ง€์ ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ํ™œ์šฉ๋œ ์•„ํŒŒํŠธ ๋‹จ์ง€ ๊ฑฐ์ฃผ๋ฏผ ํ‰๊ท  ์—ฐ๋ น๊ณผ ํ‰๊ท  ๊ฐ€๊ตฌ์› ์ˆ˜ ๋ณ€์ˆ˜๋Š” ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์…• ๋‹จ์œ„๊ฐ€ ์•„ํŒŒํŠธ ๊ณต๋™์ฃผํƒ๋‹จ์ง€์ž„์—๋„ ๋ถˆ๊ตฌํ•˜ ๊ณ  ์ž๋ฃŒ ์ˆ˜์ง‘์˜ ํ•œ๊ณ„๋กœ ์ธํ•˜์—ฌ ํ–‰์ •๋™ ๊ธฐ์ค€์˜ ์ž๋ฃŒ๋“ค์„ ๋Œ€์‹  ํ™œ์šฉ ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ ๋ณด๋‹ค ์—„๋ฐ€ํ•œ ์ž๋ฃŒ ์ˆ˜์ง‘์„ ์œ„ํ•˜์—ฌ ๋…ธ๋ ฅํ•  ํ•„์š” ๊ฐ€ ์žˆ๋‹ค.์ œ 1์žฅ ์„œ๋ก  1 ์ œ 1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ๊ณผ ํ•„์š”์„ฑ 1 ์ œ 2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„์™€ ๋ฐฉ๋ฒ• 6 1. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 6 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 7 ์ œ 3์ ˆ ์—ฐ๊ตฌ์˜ ๊ตฌ์„ฑ 9 ์ œ 2์žฅ ์Œ์‹๋ฌผ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์™€ RFID ๊ธฐ์ˆ  13 ์ œ 1์ ˆ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ 13 1. ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ ์ •์ฑ…์˜ ์˜๋ฏธ์™€ ์ข…๋ฅ˜ 13 1) ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์˜ ์˜๋ฏธ 13 2) ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์˜ ์ข…๋ฅ˜ 15 2. ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์˜ ํ•œ๊ณ„ 18 ์ œ 2์ ˆ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์™€ ์ •์ฑ…์ˆ˜๋‹จ(policy instruments) 20 1. ์ •์ฑ…์ˆ˜๋‹จ์˜ ํ•„์š”์„ฑ๊ณผ ์ •์˜ 20 2. ์ •์ฑ…์ˆ˜๋‹จ์˜ ์œ ํ˜• 20 3. McDonell๊ณผ Elmore์˜ ์ •์ฑ… ์ˆ˜๋‹จ ์œ ํ˜• 22 4. ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์™€ ์ •์ฑ… ์ˆ˜๋‹จ 27 ์ œ 3์ ˆ ํ•œ๊ตญ์˜ ์Œ์‹๋ฌผ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ 32 1. ํ•œ๊ตญ์˜ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ 32 2. RFID ๊ฐœ๋ณ„๊ณ„๋Ÿ‰ ๋ฐฉ์‹(๊ณต๋™์ฃผํƒ) 37 3. ์ฃผํƒํ˜•ํƒœ๋ณ„ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ 39 1) ๊ณต๋™์ฃผํƒ(์•„ํŒŒํŠธ) 40 2) ๊ธฐํƒ€ ๋ฐฐ์ถœ์›(๋‹จ๋…์ฃผํƒ, ์†Œํ˜•์Œ์‹์ , ๋‹ค๋Ÿ‰๋ฐฐ์ถœ์‚ฌ์—…์žฅ) 41 ์ œ 4์ ˆ RFID ๊ธฐ์ˆ  ๋„์ž…๊ณผ ์Œ์‹๋ฌผ ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ 42 1. RFID๊ธฐ์ˆ ์˜ ๋„์ž… 42 2. ์‚ฌ์šฉ๋ฐฉ๋ฒ• 43 3. ์„œ์šธ์‹œ ํ˜„ํ™ฉ 45 1) ๊ณต๋™์ฃผํƒ(์•„ํŒŒํŠธ) 45 2) ๋‹จ๋…์ฃผํƒ 46 3) ์†Œํ˜• ์Œ์‹์ ๊ณผ ๋‹ค๋Ÿ‰๋ฐฐ์ถœ์‚ฌ์—…์žฅ 47 ์ œ 5์ ˆ RFID ๊ธฐ์ˆ  48 1. RFID ๊ธฐ์ˆ ์˜ ์—ญ์‚ฌ์™€ ํ™œ์šฉ ์‚ฌ๋ก€ 48 1) RFID ๊ธฐ์ˆ ์˜ ์—ญ์‚ฌ 48 2) RFID ๊ธฐ์ˆ ์˜ ํ™œ์šฉ์‚ฌ๋ก€ 50 (1) ๊ตญ๋ฐฉ๊ณผ ๋ณด์•ˆ (Defense and Security) 50 (2) ์‹๋ณ„ (Identification) 52 (3) ํ™˜๊ฒฝ(Enviromental application) 53 (4) ๊ตํ†ต(Transportation) 55 (5) ๋ณด๊ฑด๋ณต์ง€ (Healthcare and Welfare) 56 (6) ๋†์ถ•์‚ฐ์—… (Agriculture and Livestock) 58 2. RFID ํ™œ์šฉ์˜ ๋ฌธ์ œ์  60 ์ œ 3์žฅ RFID์˜ ๋„์ž… ์š”์ธ ๋ถ„์„ 67 ์ œ 1์ ˆ ์„œ๋ก  67 ์ œ 2์ ˆ ๋ฐฐ๊ฒฝ์ด๋ก ์˜ ๊ฒ€ํ†  72 1. ๊ณผํ•™๊ธฐ์ˆ  ๋„์ž… ๊ฒฐ์ • ์š”์ธ ์ด๋ก  72 1) ๊ธฐ์ˆ ์ˆ˜์šฉ์ฃผ๊ธฐ ๋ชจํ˜• 72 2) ํ˜์‹  ํ™•์‚ฐ ์ด๋ก (Innovation Diffusion Theory) 76 3) ๊ธฐ์ˆ ์ˆ˜์šฉ๋ชจํ˜•(Technology Acceptance Model) 79 4) ์กฐ์ง-๊ธฐ์ˆ -ํ™˜๊ฒฝ๋ชจํ˜• (TOE model) 82 (1) TOE ๋ชจํ˜•์˜ ๊ฐœ์š” 82 (2) ๊ธฐ์ˆ ์  ์š”์ธ 84 (3) ์กฐ์ง์  ์š”์ธ 87 (4) ํ™˜๊ฒฝ์  ์š”์ธ 90 (5) ์ •๋ถ€์˜ ์˜ํ–ฅ๋ ฅ 91 2. ํ˜์‹ ์˜ ์ „ํŒŒ 92 1) ์บ์ฆ˜ ๋ชจํ˜•(Chasm model) 92 2) Technology Hype ๋ชจํ˜• 94 3. ์„ ํ–‰์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์™€ ๋ณธ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 99 ์ œ 3์ ˆ ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ 105 1. ์—ฐ๊ตฌ๋Œ€์ƒ ์„ค์ •๊ณผ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 105 1) ์—ฐ๊ตฌ ๋Œ€์ƒ 105 2) ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 106 2. ๊ฐ€์„ค์˜ ์„ค์ • 107 1) ๊ธฐ์ˆ ์  ์š”์ธ 107 2) ์กฐ์ง์  ์š”์ธ 109 3) ํ™˜๊ฒฝ์  ์š”์ธ 111 3. ์—ฐ๊ตฌ์˜ ๋ชจํ˜• 114 4. ๋ถ„์„ ๋ฐฉ๋ฒ• 115 1) ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„ ๋ชจํ˜• 115 2) ์‹ฌ์ธต ์ธํ„ฐ๋ทฐ 115 3) ์—ฐ๊ตฌ์˜ ํ๋ฆ„ 116 5. ๋ณ€์ˆ˜์˜ ์ธก์ • 118 ์ œ 4์ ˆ ์‹ค์ฆ๋ถ„์„ 126 1. ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 126 1) ์ข…์†๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 126 2) ๋…๋ฆฝ/ํ†ต์ œ๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 140 (1) ์—ฐ์†ํ˜• ๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 140 (2) ๋ฒ”์ฃผํ˜• ๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 143 2. ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„(logistic regression analysis) 144 ์ œ 5์ ˆ ์†Œ๊ฒฐ 150 ์ œ 4์žฅ ์„ธ๋Œ€๋ณ„ ์ข…๋Ÿ‰์ œ ๋„์ž…ํšจ๊ณผ ๋ถ„์„ 153 ์ œ 1์ ˆ ์„œ๋ก  153 ์ œ 2์ ˆ ๋ฐฐ๊ฒฝ์ด๋ก ์˜ ๊ฒ€ํ†  157 1. ๊ฒฝ์ œ์  ์ธ์„ผํ‹ฐ๋ธŒ์˜ ํ™œ์šฉ 157 2. ์ง‘๋‹จ ์ธ์„ผํ‹ฐ๋ธŒ์˜ ํšจ๊ณผ์„ฑ๊ณผ ๋ฌด์ž„์Šน์ฐจ ํ˜„์ƒ 164 3. ๊ฐœ์ธ ์ธ์„ผํ‹ฐ๋ธŒ์˜ ํ™œ์šฉ 169 4. ๊ณผํ•™๊ธฐ์ˆ ์˜ ์ •์ฑ… ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์ธ์„ผํ‹ฐ๋ธŒ 175 1) ๊ณผํ•™๊ธฐ์ˆ ์˜ ์ •์ฑ…ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ 175 2) ๊ณผํ•™๊ธฐ์ˆ ๊ณผ ์ธ์„ผํ‹ฐ๋ธŒ์˜ ์ž‘๋™ 179 5. ์„ ํ–‰์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„์„ฑ๊ณผ ๋ณธ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 180 ์ œ 3์ ˆ ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ 185 1. ์—ฐ๊ตฌ๋Œ€์ƒ ์„ค์ •๊ณผ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 185 2. ๊ฐ€์„ค์˜ ์„ค์ • 189 3. ์—ฐ๊ตฌ์˜ ๋ชจํ˜• 191 4. ๋ถ„์„ ๋ฐฉ๋ฒ• 195 5. ๋ณ€์ˆ˜์˜ ์ธก์ • 199 ์ œ 4์ ˆ ์‹ค์ฆ๋ถ„์„ 203 1. ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 203 1) ๊ฒฝํ–ฅ์ ์ˆ˜(propensity score) ๋ถ„์„ 203 2) ์ข…์†๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 206 3) ๋…๋ฆฝ/ํ†ต์ œ๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 211 2. ์ด์ค‘์ฐจ๊ฐ๋ฒ•(Difference in Difference) ๋ถ„์„ 214 ์ œ 5์ ˆ ์†Œ๊ฒฐ 219 ์ œ 5์žฅ ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ ์ธ์ƒํšจ๊ณผ ๋ถ„์„ 221 ์ œ 1์ ˆ ์„œ๋ก  221 ์ œ 2์ ˆ ๋ฐฐ๊ฒฝ์ด๋ก ์˜ ๊ฒ€ํ†  225 1. ์“ฐ๋ ˆ๊ธฐ ์ข…๋Ÿ‰์ œ์™€ ์‹œ์žฅ ์›๋ฆฌ์˜ ํ™œ์šฉ 225 2. ์˜ค์—ผ์„ธ์™€ ๋ฐฐ์ถœ ์ˆ˜์ˆ˜๋ฃŒ์˜ ๋ถ€๊ณผ 227 ์ œ 3์ ˆ ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ 231 1. ์—ฐ๊ตฌ๋Œ€์ƒ ์„ค์ •๊ณผ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 231 1) ์—ฐ๊ตฌ ๋Œ€์ƒ 231 2) ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 234 2. ๊ฐ€์„ค์˜ ์„ค์ • 234 3. ์—ฐ๊ตฌ์˜ ๋ชจํ˜• 236 4. ๋ถ„์„ ๋ฐฉ๋ฒ• 240 5. ๋ณ€์ˆ˜์˜ ์ธก์ • 242 ์ œ 4์ ˆ ์‹ค์ฆ๋ถ„์„ 245 1. ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 245 1) ์ข…์†๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 245 2) ๋…๋ฆฝ/ํ†ต์ œ๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆํ†ต๊ณ„๋ถ„์„ 254 2. ์‹œ๊ณ„์—ด ๋ถ„์„ 259 1) ์ตœ์ ์‹œ์ฐจ ์„ ์ • 259 2) ๋‹จ์œ„๊ทผ(unitroot) ๊ฒ€์ • 260 3) ์‹œ๊ณ„์—ด ๋ถ„์„ 262 ์ œ 5์ ˆ ์†Œ๊ฒฐ 270 ์ œ 6์žฅ ๊ฒฐ๋ก  273 ์ œ 1์ ˆ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 273 ์ œ 2์ ˆ ์—ฐ๊ตฌ์˜ ํ•จ์˜ 276 1. ์—ฐ๊ตฌ์˜ ์ด๋ก ์  ํ•จ์˜ 276 2. ์—ฐ๊ตฌ์˜ ์ •์ฑ…์  ํ•จ์˜ 281 ์ œ 3์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์™€ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋ฐฉํ–ฅ 284 ์ฐธ๊ณ ๋ฌธํ—Œ 287 327 ์˜๋ฌธ์ดˆ๋ก 329Docto

    Science and Innovations for Food Systems Transformation

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    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems

    Secondary cities as catalysts for nutritious diets in low- and middle-income countries

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    The world is facing a malnutrition crisis in the midst of rising rates of urbanization; more than half of the world's population lives in urban areas, a number that is expected to reach two-thirds by 2050, consuming 80% of the world's food. Instead of the development of existing cities into 'mega-cities, ' urbanization is creating a patchwork of smaller urban areas. In 2018, close to half of the world's urban residents lived in settlements or towns with less than 500, 000 inhabitants. These settlements are classified as secondary cities and are, in terms of population, the fastest growing urban areas. Poor diets among city inhabitants are the consequence of a combination of forces. These include changes in types of occupation, particularly for women; food-environment factors; shifts in norms and attitudes regarding food; globalization of food supply chains; lack of infrastructure; post-harvest food loss and waste, etc. Secondary cities offer entry points for food system transformation. Secondary cities are characterized by strong urban-rural linkages and the opportunity for localized food production and consumption. These cities could also play a key role in enhancing resilience to food security shocks. This chapter discusses the challenge of the growing triple burden of malnutrition in urban contexts and argues for the important role of secondary cities in transforming urban food systems. Through three case studies of secondary cities in LMICs, these cities are shown as emerging players in nutrition-centered food system interventions. ยฉ The Author(s) 2023

    Science and Innovations for Food Systems Transformation

    Get PDF
    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems

    Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

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    This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems
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