Abstract

Keywords

Introduction

Smart electric pole

Sustainable power unit

Conclusion

Data Availability Statement

Declaration of Competing Interest

References

Abstract

With the effective result of IoT architecture in all research areas, we propose IoT framework based energy efficient smart and intelligent street road lighting system that consist of IoT sensor bases smart electric pole with controller for tuning LED lamps. In our work we replace traditional metal halide lamps with mesophic design LED lamps based on human eye sensitivity and it provides significant saving of energy too. Based on the sensor unit information about traffic flow and presence and absence of occupant smart and intelligent decision making module compute intensity level that generate different width pulse using PWM dimming system which triggers LED power switch via DALI controller mounted inside LED Light Controller. For efficient use of sustainable energy resources we use sustainable power systems that consist of PV solar panel units, Battery storage systems and smart electric power grids. For the charging battery system we use MPPT based dynamic battery charging algorithm. From experiment and simulated result, we observe that proposed energy efficient Smart Street lighting saves considerable amount of energy during peak and off peak hours not only at highway roads but at residential and sub-urban pedestrian areas also.

Introduction

Sustainable energy is the method of utilizing energy in an efficient way and at the same time satisfying the needs of the modern world. Sustainable energy can be used in economic, social and environmental issues. An efficient sustainable energy management system can be achieved by using renewable resources in all the possible ways, using sustainable energy resources, using alternative energy resources, making use of smart systems, implementing IoT systems, implementing green technologies. The smart system is the technique, which is used to embed the modern technology into functions already used by the world. In terms of smart system, the information about utility system and traffic flow of the large cities is extracted with the help of sensor also it analysis the pattern of information for prediction. Then, the ability to transfer data over a network without the help of human-to-computer or human-to-human interaction is called IoT or internet of things system and the green technology is used to explain the functions of science and technology to develop products as environmental friendly. Some of the examples of green technology is water purification, conserve natural resources, clan energy creation and waste recycle management. It is also used in digital and mechanical machines, objects and in correlated computing devices.

ABSTRACT

1. Introduction

3. Research methodology

4. Sample data, sources, and characteristics

5. Empirical results

6. Conclusions

Declarations

References

Abstract

This paper examines whether the 1997 Asian financial crisis affected the renewable energy/carbon dioxide (CO2) emissions relationship differently when compared to the 2008 global financial crises. Using the Dynamic Panel Data Model, we examine separately the impact of the 1997 crisis and the 2008 crises on the stated relationship for annual data between the 1987–2018 period for a group of high, upper-middle, and lower middle-income countries. Our findings suggest that the results were crisis and country specific. For the overall sample, the relationship between the two variables was positive (and significant post-1997 and pre-2008 crises) but negative post-2008 crisis. In contrast, the positive relationship remained unchanged for the lower middle-income subsample through the two crises. We also find evidence that the 1997 Asian crisis altered the relationship differently than the 2008 financial crisis especially for the upper and middle-income groups. Clearly, reduction of CO2 emissions may not be guaranteed even if host countries adopt renewable energy sources since country income levels and the nature of the crisis may matter. Future research may consider how the degree of pollution controls and differential costs of renewable energy adoption in countries may alter this relationship.

1. Introduction

Research examining the renewable energy consumption/carbon dioxide (CO2) emission links for developing countries are extensive and generally recommend policies to ratchet up the local renewable energy infrastructure to encourage renewable energy consumption (Pao and Tsai, 2011; Shahbaz et al., 2013; Zhu et al., 2016), particularly for lower income countries to allow them to attract foreign direct investment (FDI) aimed at inflows of technology transfer (Omri and Kahouli, 2014; Doytch and Narayan, 2016). Extant literature also documents links between global financial crises and the transmission of technological innovation to recipient countries. For instance, Colombo et al., 2016, Zouaghi and Sánchez, 2016, and Zouaghi et al., 2018 show the firms devise survival and growth strategies designed to overcome global financial crises by developing innovation products. Although there is extensive work on the factors that affect the renewable energy adoption/CO2 emissions relationship, there is scant work (with few recent exceptions) on how a crisis will alter this relationship. The exception includes recent work by Dong et al., 2020a who show that countries switching to renewable energy sources reduced CO2 emissions (but not statistically significantly) post-2008 crisis 1 versus pre-2008 crisis levels for a sample of 120 countries.

In this paper, we add to the emerging literature by examining whether the renewable energy/pollution links were also similarly altered during the 1997 crisis. We conjecture that the impact of the 2008 crisis affected global economies differently than the 1997 crisis. Hence, we conjecture that the differential effects on country macroeconomic variables can imply differences in the relationship between renewable energy adoption and CO2 emissions pre and post each crisis. If the pre/post link changes were different for the 1997 crisis versus the 2008 crisis, then policy prescriptions useful for one crisis may not work for other crises. We don’t believe that this issue has been investigated in the extant literature. Specifically, we conjecture that the renewable energy consumption – CO2 emission relationship was altered differently by the 1997 crisis than by the 2008 crisis and may also be a function of the level of economic development of host countries.

Different from many studies on Energy Kuznets Curve, this paper does not directly consider income factor but conducts a nonlinear study on per capita primary energy consumption and electricity consumption in China, whose economic development is considered investment-driven, in which government macroeconomic policies, especially government expenditure plays a very important role. In particular, this paper attempts to compare the different impacts of government expenditures and the other influencing factors on energy consumption. Based on the results of the threshold regression, in the future, with the further development of the economy, the driving force of labor population growth will slow down. Comparing with primary energy, the relationship between electricity consumption and its influencing factors will change later, and the difference in the effects of the influencing factors is mainly reflected in the formation of capital stock (investment) and the development of urbanization. Government expenditure on private goods may not be always as inefficient in energy consumption and cleaner production as previously thought in the literature. Meanwhile, the growth of government expenditure on public goods will become a powerful driving factor which indicates that the government needs to assess its overall impact on energy consumption and energy efficiency when participating in the provision of public goods. The paper also found that when studying the issue of energy consumption and regional differences in China, the geographic classification of eastern, central and western regions may not be appropriate. Grouping according to energy consumption levels may be one of the better options. And the distribution of energy consumption levels in China’s provinces is consistent with the distribution of government expenditure levels. The estimation results also provide us with important policy implications: the government expenditure in education is of great importance, and it can produce a strong energy-saving effect through technological progress.

Introduction

The rapid growth of China’s energy consumption in recent decades is linked to the rapid growth of the economy. At the same time, an important feature of China’s economic development is investment-driven, in which government macroeconomic policies, especially government expenditure, plays a very important role. According to World Bank statistics (WordBank, 2019), as shown in Figure 1, the intuitive feeling is that China’s large-scale expansion of government expenditure since 1978 has been accompanied by rapid growth in energy consumption (Wang and Lin, 2019). Figure 1 demonstrates that the growth of government expenditure and capital formation is highly volatile. For example, in 1984, 1992 and 1999, there was a sharp growth in government expenditure around these years. These years are also years in which the private sector is also encouraged to invest to expand domestic demand. During such years, energy consumption, especially electricity consumption, seems to have strong consistency with these variables.

In the field of algorithms and data structures analysis and design, most of the researchers focus only on the space/time trade-off, and little attention has been paid to energy consumption. Moreover, most of the efforts in the field of Green Computing have been devoted to hardware-related issues, being green software in its infancy. Optimizing the usage of computing resources, minimizing power consumption or increasing battery life are some of the goals of this field of research. As an attempt to address the most recent sustainability challenges, we must incorporate the energy consumption as a first-class constraint when designing new compact data structures. Thus, as a preliminary work to reach that goal, we first need to understand the factors that impact on the energy consumption and their relation with compression. In this work, we study the energy consumption required by several integer vector representations. We execute typical operations over datasets of different nature. We can see that, as commonly believed, energy consumption is highly related to the time required by the process, but not always. We analyze other parameters, such as number of instructions, number of CPU cycles, memory loads, among others.

Introduction

We are surrounded by digital information, such as the huge amount of data generated on the Internet and also that we are collecting in our daily lives: human generated data, both consciously (such as emails, tweets, pictures, voice) and unconsciously (clicks, likes, follows, logs, . . . ), or observed data (biological, astronomical, etc.). When managing large volumes of digital information, data compression has always been considered to be vitally important. Traditionally, data compression focused on obtaining the smallest representation possible, in order to save space and transmission time, thus, providing a good archival method. However, most of the compression techniques require decompressing the data when they need to be accessed, especially when these accesses are not sequential, and thus, limiting the applicability of data compression. To overcome these issues, compact data structures appeared in the 1990’s and rapidly evolved during early years of the current century [1]. They use compression strategies to reduce the size of the stored data, taking advantage of the patterns existing in the data, but with a key difference: data can be directly managed and queried in compressed form, without requiring prior decompression. The main contribution is that they allow larger datasets fit in faster levels of the memory hierarchy than classical representations, thus, dramatically improving processing times. In addition, many compact data structures are equipped with additional information that, within the same compressed space, acts as index and speeds up queries.

1- Introduction

2- Research approaches

3- BEC and SEC correlations

4- Conclusions

References

Abstract

According to the 2015 Thai Energy Conservation Plan, the government aims to reduce energy consumption by 37% in 2036 compared to the final energy consumption in 2010. The industry sector consumes about 35.7% of total energy consumption. Non-metallic sector accounts for 30.74% of the energy consumption of the industry sector. The cement industry is one of the non-metallic sector that consumes up to 60.69% of the energy consumption of the non-metallic sector. Specific energy consumption (SEC) and Baseline energy consumption (BEC) are used as a tool to assign energy conservation target for each industry sector. This paper presents BEC and SEC for cement production. There are 12 of 21 participating cement plants that provide relevant information to be used for BEC and SEC analysis. According to the analysis, the BEC and SEC of cement production does not depend only on the quantity produced, but also ambient temperature and humidity. Therefore, setting a typical BEC and/or SEC target for reducing energy consumption is not appropriate and unfair to the factory. The BEC and SEC equations are developed using statistical approaches. The cement productions are classified into 2 categories; clinker and cement. Regression equations are developed to determine the relationship between BEC/SEC and relevant variables, including BEC/SEC for electrical energy, thermal energy and total energy.

Conclusions

The cement industry is the most energy-consumed industry subsector which consumes almost 60.69% of nonmetallic sector. The cement industry is one of target subsectors to promote and regulate energy efficiency measures to achieve the EEP 2015 target. Energy consumed are divided into two portions by products, i.e., one to produce clinker and the other to produce cement. Most of the energy are used to produce clinker by which both thermal and electrical energy are needed. For cement, only electrical energy are required. Regression equations to determine BEC and SEC are developed for clinker and cement products in Thailand. They can be further used as benchmarks for energy consumption in this sector. Data for analysis and regression are gathered and measured from 12 of 25 cement plants. BEC and SEC equations are developed based on products, i.e., clinker and cement. The results show that the equation can predict the value within acceptable tolerances. BEC and SEC depend on three variables, i.e., quantity of product, yearly-averaged ambient temperature and yearly-averaged ambient humidity. For each particular year, data for each variable are gathered and substituted into the equations to calculate BEC and SEC that will be further used as benchmarking. They will be compared with the actual BEC and SEC of the same year to determine whether the energy usage is effective or not.

Solar energy radiation is essential in plant physiology and pathophysiology hence its knowledge is fundamental for example, to estimate evapotranspiration (Gocić et al., 2015; Petković et al., 2015) and to predict infection risk of some fungus diseases (Katsantonis et al., 2017; Dalla Marta et al., 2008) that are needed to schedule irrigation and fungicide spraying. Solar energy radiation is usually expressed in terms of the energy flux density through a horizontal area (irradiance) and an integrated value over one day (daily solar insolation) fits these applications. For precision agriculture, high accuracy measurements are not required and manufactures recommend the use of photodiode-based pyranometers which are cheaper than thermopile-based pyranometers (Kipp&Zonen, 2018). However, they still cost hundreds of euros and consume some milliwatts hence do not suit low-cost wireless sensor nodes. As an alternative, insolation values in field studies are usually obtained from public weather stations often far away from the crop of interest. This results in errors due to the inhomogeneous solar energy distribution caused by orography, competing vegetation or clouds (Reuter et al., 2005). For extended areas, more reliable in-field data would be better obtained from wireless sensor networks that include solar radiation sensors but this can be thwarted by cost and power consumption constraints (Wang et al., 2006). In order to overcome these constraints, we propose to use the components already integrated into the solar energy harvester of sensor nodes to measure solar radiation too. During the last decade, small PV panels have been used as low-cost radiation sensors to monitor PV solar plants. Solar irradiance has been deduced from the voltage drop across a resistor biased by a PV panel operating near short-circuit condition (Husain et al., 2011). Short-circuit current is approximately proportional to solar irradiance hence a way to estimate it, but unfortunately the power yield is null at this operating point. Further, the temperature drift of the sensitivity of the PV panel to solar irradiance must be considered. An obvious solution to compensate for temperature drift is to include a temperature sensor (Carrasco et al., 2014; Mancilla-David et al., 2014; Ma et al., 2017) but the sensor and its conditioning circuits add cost. An alternative solution is to measure the open-circuit voltage and the short-circuit current of the PV panel (Ortiz Rivera and Peng, 2006; da Costa et al., 2014). The temperature coefficient of the short-circuit current is positive whereas that of the open-circuit voltage is negative, and both increase with solar irradiance, which leads to a bijective function between them that can be obtained from a physical model for the PV panels. Unfortunately, the calculation is performed by iterative complex algorithms that require DSPs, and current sensors that do not suit low-power solar energy harvesters because of cost and power consumption constraints.

Industrial revolution and rapid economic growth in countries have resulted into day’s much discussed phenomena like global warming and climate change. The global increasing concern for environmental sustainability is visible in a public arena; the development strategy depends largely on economic growth which causes environmental degradation. In the past two decades, Kaygusuz (2009) has indicated the increase of carbon dioxide emissions is considered one of the main causes of global warming and climatic instability. The prevalence of such problems is higher in countries such as Tunisia, where economic growth, energy security and environmental sustainability are simultaneously important. On the other hand, the use of energy natural resources as inputs in the development or production process is a problem. Tunisia, as a developing country, economic growth represents an interesting case where it faces the difficulty to fulfill the needs of energy demand. Industries may increase pollution in developing countries such as Tunisia, due to the increased production of emissions intensity to export goods to developed countries. Currently, the economy of Tunisia is diverse economy, ranging from agriculture, fisheries, mining, manufacturing, textiles, and petroleum products, to tourism. Tunisia is not like some North African countries as Algeria and Libya which are mostly endowed with rich hydrocarbon resources. Tunisia is endowed with multi and excellent renewable resources. Thus, Tunisia should be more keen to invest in renewable energy generation projects to minimize spending and dependence on foreign energy sources. Therefore, analyses of economic and environmental impacts of concerned variables are needed to address this research gap and to improve the design and accept the energy renewable policy. The main objective of present study is to examine the long run and the relationship between economic growth, renewable energy consumption, energy consumption and environmental pollution measured by CO2 emissions in Tunisia during the period 1990–2015. This object is achieved by the following steps: First, stationarity and Johansen co-integration are tested; second, error-correction models are estimated to test for the Granger causality between the two variables. The rest of this article is structured as follows: After the section (1) which is the introduction, this paper includes five additional sections: section (2) is a literature review; section (3) presents the data source and methodological framework; section (4) presents the empirical results and interpretation. finally, section (5) present the conclusion and policy implications.

With the rapid development of new-generation information technology (NGIT), especially the deep integration of NGIT and manufacturing technology, great changes have taken place in the manufacturing service environment. Meanwhile, the energy usage of the manufacturing industry is intensifying, which results in more and more serious energy crisis. On the other hand, the energy consumption of a product in the entire life cycle is mostly decided by the design and manufacture process. So, energy-saving and emission-reduction is a key issue in design and manufacturing processes of a new product which must meet customers’ demands and also reduce energy consumption and environmental impact (Zhang and Li 2010). Energy consumption evaluation and analysis (ECEA) refers to the collection of energy consumption data of a product in its entire life cycle, the construction of the corresponding evaluation model, and the visualisation analysis of the evaluation result. Its aim is to achieve statistical management of energy consumption, the analysis of key energy consumption points and to provide data support for decision-making. Thus, an effective ECEA approach of product life cycle is paramount for realising the above aims. In order to realise accurate ECEA, real-time and dynamic data are needed in the energy management of a product life cycle (Reap et al. 2008; Taisch, Cammarino, and Cassina 2011). However, current research mostly focuses on the way to obtain data from the energy consumption database, such as Chinese Life Cycle Database (CLCD) from China, Econvient from Switzerland, and European Reference Life Cycle Database (ELCD) from the European Union. These data, which come from industry statistics or technical literature, are mainly historical data. It is difficult to provide sufficient support for accurate analysis and decision-making of energy consumption for a current product (Seow, Rahimifard, and Woolley 2013). It has very important impact on product energy-saving. On the other hand, as the ECEA system is established from the perspective of product life cycle, the required data are necessarily related to the enterprise information systems (EISs) in the entire product life cycle. Therefore, in order to ensure data validity, the integration between ECEA system and EISs should also be considered. From the point of view of realising ECEA, many methods such as life cycle assessment (LCA) (Finnveden et al. 2009; Göschel, Schieck, and Schönherr 2012),economic input–output-LCA (Joshi 1999), life cycle costing (Gluch and Baumann 2004), life cycle simulation (Umeda et al. 2012), life cycle sustainability assessment (Kloepffer 2008), social life cycle assessment (Jørgensen et al. 2008), embodied energy model (Kara, Manmek, and Herrmann 2010) have been proposed to assess the environmental impacts and costs of product entire life cycle stages consisting of material extraction, manufacturing process, use, maintenance, etc. These methods have a very important role for energy-saving in the entire product life cycle. However, from the perspective of the users in practical application processes, the following issues still need to be addressed.

Over the last two decades, climate change has emerged as a major concern for academics, practitioners and governments due to the threat it poses to existence (Shove et al., 2015). Researchers around the world have, in no uncertain terms, stated that countries must reduce the emission of greenhouse gases or suffer potentially catastrophic effects from climate change (Chandel et al., 2016; Kondoh, 2009; Sarzynski et al., 2012). Since combustion of fossil fuels for energy generation is the prime source of greenhouse emissions, many scholars assert that a shift towards green energy is imperative to achieving environmental goals (Sarzynski et al., 2012). Zarnikau (2003) has defined green energy as “electricity generated using renewable energy sources, and including technologies such as photovoltaic solar panels, biomass projects, geothermal projects and wind farms” (p.1661). The usage of green energy sources for electricity generation involves zero greenhouse gas emission, thereby offering a lasting solution to climate change (Kostakis and Sardianou, 2012). Hence, green energy has attracted significant research attention around the globe. According to Herbes and Ramme (2014), renewable sources of energy may be utilized to successfully fight global warming. Governments around the world also have shifted focus towards generating electricity from renewable sources of energy (Shrimali and Kniefel, 2011). In several countries, incentive programs such as feed-in-tariffs, captive generation, and favoured access to grids for renewable energy producers have been initiated to encourage private investment. In fact, the Dutch government liberalized the green electricity market and offered relatively generous fiscal incentives to stimulate demand for green energy in the residential customer segment also. The Government of India has also, along similar lines, launched various incentives schemes such as generation based incentives, accelerated depreciation benefit, renewable energy certificates and captive generation to encourage establishment of renewable energy generating facilities (Shrimali et al., 2013).

Energy is indispensable for the economic development of various countries in the world. It contributes to economic development and the improvement of technical efficiency in the short run (Birgit et al., 2016). In the past, high energy consumption resulted in high investment and high pollution. The long-standing low energy-utilization efficiency and high environment pollution have had a strong impact, particularly on economic development (Sanford, 2015; Francesco et al., 2016). Strategic emerging industries are the new breakthroughs and growth poles for driving regional economic development (Dan, 2016). However, strategic emerging industries still face multiple constraints if they are expected to be the leading industries or pillar industries. The developmental trajectories of traditional industries lack the critical core technologies for industrial development (Florian, 2015). A good mastery of industrial core technologies is crucial to the development of strategic emerging industries; energy utilization efficiency must be rapidly improved by the industrial technological innovation so that the industrial growth and expansion are also rapidly promoted (Jagjit et al., 2016; Hans et al., 2016). However, the driving effect of technological innovation on energy utilization efficiency is mainly theoretical, empirical studies are still lacking. From this new perspective, the paper analyzes the influencing relationship between technological innovation and energy utilization efficiency of strategic emerging industries, gets the positive and negative influencing factors. The technological innovation model can be proposed to improve energy utilization efficiency, it is of great theoretical significance and practical value. The extensive development mode leads to excessive consumption of energy, exhaustion of limited energy, and deterioration of the beautiful environment (Tommy et al., 2016). Thus, various countries in the world need a new growth pattern, i.e., an intensive growth pattern. In this historical reformation, various countries propose developing strategic emerging industries, accelerating industrial structure adjustments and upgrades, transforming the economic growth pattern, and leading and guiding the economic society to achieve long-term development (Li-yan Sun et al., 2017; C. Vasanthakumar et al., 2016). To achieve such a transformation, it is imperative to strengthen the technology-innovation construction of strategic emerging industries, attach importance to technological-innovation issues, and improve the labor productivity and technological innovation efficiency based on the role of technological innovation in driving the energy-utilization efficiency of strategic emerging industries (Gustav et al., 2016). Technological innovation is one of the critical impetuses for strategic emerging industries to improve energy utilization efficiency (Ulf Andersson et al., 2016; Piret, 2015). Technology innovation not only has a strong impact on the production modes of strategic emerging industries, but also plays roles in reforming the technological paradigm and continuously developing ecological production modes and ecological technologies (Bronwyn and Christian, 2013). To create sustainable utilization of energy needs means that strategic emerging industries must raise energy-utilization efficiency by relying on technological innovation to promote the economic transition and development when achieving energy conservation and emission reduction (Kassahun and Toshio, 2017; Wolfgang et al., 2016). However, the impetuses for traditional economic and social development in the world are high energy consumption and high investment rather than the improvement of technological innovation (Masachika, 2015).

مقاله سال 2016

China’s economic momentum has spiked aggressively in recent years, and the country continues to play a major role in the process of economic globalization. Following such rapid development, environmental damage has become a severe problem and the availability of natural resources has sharply declined. The Chinese Government has increased investment in energy infrastructure construction and pushed for new technologies to supply power more effectively. Energy resources are materials and natural processes that have the ability to provide widespread power under present socioeconomic conditions. Coal and petroleum are two relevant modern energy resources.

Although production efficiency has greatly improved since the Industrial Revolution, the main resources for developing machine production, such as iron ore, coal and petroleum, are still in short supply. Due to its energy-intensive economy, China has the highest national annual energy consumption (Statistical Review of World Energy, 2015). From 2012 to 2013, China’s annual energy consumption increased by 15.4%, to 3.75 billion tons of standard coal equivalent (TCE), of which coal, oil and natural gas accounted for 68%, 19% and 4.4%, respectively (China Statistical Yearbook, 2015). In 2012, China’s energy consumption per 10,000 Yuan GDP was 0.76 TCE, a 6% decrease compared with 2010. This is a result of China recently advocating the development of renewable and clean energy while decreasing the use of nonrenewable energy. It is important to note that many people continue to use less efficient energy sources, such as coal and oil, both of which provide little energy for pollution they create. Despite this, alternative energies with high efficiency and low environmental impact are also becoming more prevalent, including geothermal and hydropower systems, nuclear power, wind power and natural gas.

China’s rapid economic growth and associated high energyconsumption in both imply huge social costs, including water pollution, health and safety problems, poor air quality, and resource shortages on both local and global scales (He et al., 2002). For example, Song et al. (2015a) demonstrated that the economic scale effect significantly explained the increased carbon emissions in the Yangtze River Delta region. Among 500 developed cities in China, fewer than five achieve the World Health Organization standards for air quality. China contains some of the most polluted places in the world. The main reason for these problems is the emission of sulfur dioxide, nitrogen oxides, and smoke from heavy chemical industries, coal plants and the overexploitation of natural resources.