Search

This paper investigates the sustainability of Tabriz City focusing on its decarbonization. Statistical, quantitative, and comparative research methods are used to analyze the total energy consumed, conversion of primary energy, final exergy, and losses analysis according to the renewable and carbon-based energies. Then, the renewable resource potentials of the city are simulated using Grasshopper plug-in-Rhino simulator, THERM, PVSyst photovoltaic software, and ArcMap. In terms of the socio-economic assessment, non-oil Per Capita GDP, emissions, and energy intensities are analyzed concerning the B2DS scenario by 2050. The per capita GDP of the city is 71% of the country's average value. Tabriz's real GDP growth averaged −1.11% per year, primary energy demand increased by an average of 6.1%, CO 2 emissions from energy use grew by an average of 6% annually and the emission intensity of the economy increased by a total of 52.02%, in the period from 2011 to 2019. Finally, it is shown that the city consumes 58050 GWh/year of different carbon-based resources that only produced 20110 GWh/year of useful work with 5.63 MT CO 2 emissions, while the same work could locally be supplied by 30000 GWh/year RE with a minimum rate of 0.18 MT CO 2 emissions. The city Landfill receives 1000 tons/day of MSW, but two-thirds of this amount can be recycled. Regarding different sectors, buildings produce half of the CO 2. The cement industry with dual pollutants (17%) is the most polluting factory. Carbon-based electricity (66%) is the most polluting form of energy. In terms of carbon sequestration requirements with trees, shifting from fossil fuels toward green energies, EE, and recycling MSW can plunge required trees from 81.9 to 1.16 million trees. This paper highlights the importance of electrification of transportation based on RE, energy performance of buildings, and all sectors. • Large city decarbonization strategies • Application of RE according to geographic and climatic factors. • Electrification of sectors based on RE locally. • MSW recycling and sequestration by trees for decarbonization regarding the LULC and NDVI analysis. • Conversion and storage efficiencies of carbon-based fuels vs RE. [ABSTRACT FROM AUTHOR]

Abstract While the empirical literature on energy intensity is focused mainly on reduced energy intensity, this study examines a case where energy intensity is increasing. The paper aims at identifying price and non-price determinants of Iran's energy intensity and analyzing their importance in dynamics of it. In order to answer the research question, LMDI decomposition technique as well as time series econometrics have been used to data analysis. Based on the results, both structural changes in economic activities and inefficiency of energy consumption have led to the increase in Iran's energy intensity, although the latter dominated the former. The elasticity of energy intensity with respect to total factor productivity, real energy prices, and industrial development is negative in the short-run and long-run. Additionally, per capita income has a positive linear relationship with energy intensity that implies a high income elasticity of energy demand. Furthermore, changes in TFP and energy prices contribute more than two-thirds of forecast error variance decomposition of energy intensity, which shows the importance of these factors in energy intensity dynamics. Hence, in order to change the increasing trend of energy intensity, it is important to consider the necessities of improving technology and productivity while realizing energy prices. Highlights • Iran's economy faced an average annual growth of 3.3% in energy intensity between 1974 and 2014. • Both inefficiency and structural effects led to the increase in energy intensity of Iran. • Relative energy price and total factor productivity have key roles in energy intensity dynamics. • Economic growth based on TFP improvement will lead to declining energy intensity. • Industrial development strategy can has a significant role in energy productivity in Iran. [ABSTRACT FROM AUTHOR]

Energy drives national economies with a positive correlation. Global environmental concerns, growth of the world population, and emerging of developing economies with more need for energy resources, would change the future of energy markets and human lives. To manage energy shortage and energy-related environmental threats, decision-makers have to decide between three potential solutions [1], to develop supply value [2], to suppress energy (all types) demands, or [3] to adopt a hybrid strategy. This paper is aimed to uncover potential saving capacities to fill the supply-demand gap based on efficiency improvements for the case of Iran as a developing economy with increasing energy needs. The methodology has been designed based on energy balance calculations which have been evaluated and validated through 47 expert panels under 9 major themes. Then, policy sets have been determined and to map how defined policies were interacting in the living energy, a system interpretive structural model was performed to determine direct and indirect relationships among policies. As a result, policymakers can prioritize and set a roadmap to approach the desired future. Findings showed for the case of supply-side Iranian energy management [1] maximizing electricity transmission and distribution efficiency [2], upgrading the consumption standard in equipment and processes (in the terms of energy production, conversion, and transmission), and [3] online monitoring of distribution networks are most basic and top priority policies. While for the case of demand-side "targeting energy subsidies" is the most crucial policy to support the Iranian energy efficiency improvement targets. • Iran's potential saving capacities to fill the supply-demand gap based on efficiency improvements are studied. • Policy sets have been proposed through 47 expert panels containing 9 main themes. • An interpretive structural model was performed to map policy relationships and dedicate a macro-level action plan. [ABSTRACT FROM AUTHOR]

• Developing novel multi-level benchmarks. • Identifying benchmarks for electricity and thermal energy for office buildings. • Presentation a base for short/long-term energy efficiency plans. • Analyzing energy efficiency and consumption in details. • Creating a dataset, including building and energy information. Energy benchmarking is an accurate tool to measure, monitor, and reduce end-use energy consumption in the building sector using comparison scenarios. Various studies have applied Bottom-Up energy consumption assessment to compare the energy performance of a group of buildings with a benchmark. The Bottom-Up method mostly relies on simulating an ideal building to develop a benchmark. However, this study has developed a Top-Down energy benchmarking methodology based on the actual energy consumption within a cluster of governmental office buildings. The method presents multi-level benchmarks to provide a detailed policy for improving the energy efficiency of buildings in the short or long-term. In an exploratory study, 26 office buildings in a cold climate region were investigated to identify the multi-level benchmarks. Four general benchmark levels were developed, including the 'Best Practice, 'Good Practice', 'Benchmark', and 'Poor Practice'. In addition, 15 energy efficiency classes were also introduced and applied as a base for developing the 'Energy Performance Certificate' method. The results indicate that the benchmark values for electricity and natural gas are 40 and 252 kWh/m2/yr, respectively, while the total energy benchmark is 292 kWh/m2/yr. According to the benchmarking results, 69% of the case studies were inefficient, 23% were labeled 'C', and no cases were labeled 'A' or 'B'. The multi-level benchmarking system can provide quick and clear guidance for building designers, operators, and government regulation/enforcement agencies; thus, it can apply at local, regional, and international levels. These benchmark levels establish reference points for measuring and rewarding good performance, on the other hand, it recognizes poor-performance buildings and prioritizes them for energy efficiency improvement. The method can be replicated in various climates and urban scales. [ABSTRACT FROM AUTHOR]