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Microsoft PowerPoint - 02June2021_BMU.pptx Draft results: Modelling Carbon Neutrality - BMU 02 June 2021 ENERGY Modeling Results: BMU The path to carbon neutrality M il li o n t o n n e s o f C O 2 Cumulative mitigation steps from REF to CN (as seen by an observer in 2050) ENERGY Modeling Results: BMU Carbon emissions -50 0 50 100 150 200 250 300 350 400 450 2010 2015 2020 2025 2030 2035 2040 2045 2050 E n e rg y s y st e m C O 2 e m is si o n s [M t/ y e a r] CO2 emissions by scenario - BMU [Million tonnes/year] ENERGY Modeling Results: BMU Sector emissions 0 50 100 150 200 250 300 2020 2025 2030 2035 2040 2045 2050 0 50 100 150 200 250 300 2020 2025 2030 2035 2040 2045 2050 Carbon emissions by sector Reference (REF) Neutrality (CN) Industry Resident/Commercial Transportation Electricity Heat Fuel supply M il li o n t o n n e s C O 2 [M t C O 2 ] M il li o n t o n n e s C O 2 [M t C O 2 ] ENERGY Modeling Results: BMU Electricity generation 0 50 100 150 200 250 300 350 400 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal Electricity generation by technology - BMU Neutrality Scenario ENERGY Modeling Results: BMU Electricity generation Electricity generation by technology - BMU REF Scenario 0 50 100 150 200 250 300 350 400 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal ENERGY Modeling Results: BMU Electricity generation - 300 - 200 - 100 0 100 200 300 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal Electricity generation by technology - BMU CN-UNECE versus REF Scenario ENERGY Modeling Results: BMU The path to carbon neutrality Carbon capture, utilization and storage (sequestration) A mixed set of measures 0 10 20 30 40 50 60 70 80 90 100 2010 2015 2020 2025 2030 2035 2040 2045 2050 M tC O 2 /y r Biomass CCS Fossil CCS Industrial processes Land use ENERGY Modeling Results: BMU Final Energy Mix 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 2010 2015 2020 2025 2030 2035 2040 2045 2050 F in a l e n e rg y [ E J] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 2010 2015 2020 2025 2030 2035 2040 2045 2050 F in a l e n e rg y [ E J] Oil-liquids Bio-liquids Coal-liquids Gas-liquids Gas Hydrogen Elec Other Neutrality (CN)Reference (REF) Final energy mix - Transportation ENERGY Modeling Results: BMU Final Energy Mix Final energy mix - BMU REF Scenario 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 2010 2015 2020 2025 2030 2035 2040 2045 2050 F in a l e n e rg y [ E J] Solar Geothermal Heat Electricity Hydrogen Gas Liquids Biomass Coal ENERGY Modeling Results: BMU Final Energy Mix Final energy mix - BMU CN-UNECE Scenario 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 2010 2015 2020 2025 2030 2035 2040 2045 2050 F in a l e n e rg y [ E J] Solar Geothermal Heat Electricity Hydrogen Gas Liquids Biomass Coal ENERGY Modeling Results: BMU Final Energy Mix Final energy mix - BMU CN-UNECE versus REF Scenario -2.0 -1.5 -1.0 -0.5 0.0 0.5 2010 2015 2020 2025 2030 2035 2040 2045 2050 E J Solar Geothermal Heat Electricity Hydrogen Gas Liquids Biomass Coal ENERGY Modeling Results: BMU Investment needs Cumulative investments 2020-2050: 419.4 billion US$2020 Reference (REF) Extraction fossil fuel 112 Coal 18 Oil 2 Gas 10 Nuclear 11 Hydro 3 Biomass Geothermal Solar 6 Wind 18 T&D 166 Heat supply 12 Hydrogen 2 Other 59 Electricity 69 Extraction fossil fuel Coal Coal CCS Oil Oil CCS Gas Gas CCS Nuclear Hydro Biomass Biomass CCS Geothermal Solar Wind T&D Energy efficiency Heat supply Hydrogen Other ENERGY Modeling Results: BMU Investment needs Cumulative investments 2020-2050: 668.9 billion US$2020 Neutrality (CN) Extraction fossil fuel 62.9 Coal 0.0 Coal CCS 0.5 Oil 0.0 Gas 16.7 Gas CCS 8.2 Nuclear 16.0 Hydro 45.1 Biomass 0.2 Biomass CCS 0.0 Geothermal 0.0Solar 49.0 Wind 49.7 T&D 201.1 Energy efficiency 149.8 Heat supply 11.5 Hydrogen 10.9 Other 47.1 Electricity 185.4 Extraction fossil fuel Coal Coal CCS Oil Oil CCS Gas Gas CCS Nuclear Hydro Biomass Biomass CCS Geothermal Solar Wind T&D Energy efficiency Heat supply Hydrogen Other ENERGY Modeling Results: BMU Investment needs Cumulative investment requirements REF, CN and 2-degree  T/D & S: transmission, distribution and storage of electricity and district heat  CCS: carbon capture and storage  BAT: Best available technology 0 100 200 300 400 500 600 700 800 Reference Neutrality 2-degree Energy efficiency & intensity T/D & S Nuclear Renewables (incl. biomass CCS) Hydrogen Fossil CCS Fossil electricity generation ENERGY Modeling Results: BMU Impact of different futures Indicators across scenarios (averages between 2020 and 2050) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Final energy Intensity Energy expenditure per GDP Total cost of energy sector per GDP CO2 emissions per GDP Share of non renewables in FE Energy exports per PE Carbon intensity per kWh Reference Neutrality 2-degree
Language:English
Score: 1107290.6 - https://unece.org/sites/defaul...ova%2C%20Ukraine%20results.pdf
Data Source: un
Appliances Presentation Manufacturer’s Perspective on Energy Efficiency Standard and Labelling By: Praphad Phodhivorakhun Kang Yong Electric Public Company Limited, Thailand Mission = the government assigned to the Electricity Generating Authority of Thailand (EGAT) to promote the efficient use of electricity in Thailand “Let’s join hands to save electricity” • September 20, 1993 is the 109th anniversary. • “Let’s join hands to save electricity by use the new thin Tube fluorescent lamp” • 5 major refrigerator manufacturers in Thailand –Mitsubishi, National, Toshiba, Sanyo and Hitachi • The proportion of refrigerator sale of one-door model is about 85% and two door model is about 15%. • The proportion of refrigerator sale of one-door model are as follow: • Size 6 cubic-foot (170-180 liter) with the highest sale of 60-65% • Size 4 cubic-foot (120-130 liter) with the highest sale of 20%. • Size 2 cubic-foot (60-70 liter) has uncertainly sale volume based on business condition of the hotel and real estate with the proportion of 5%. • The other sizes such as 5 cubic-foot (140-155 liter), size 7 cubic-foot (190-200 liter), and larger than 8 cubic-foot (230-250 liter) together they has the sale proportion of 10%. • The consumption refrigerator product increased 10% annually. EGAT and the representatives from the refrigerator producers started to use the energy saving label with the one-door model refrigerator (size 6 cubic-foot) September 20, 1994, 14 models on refrigerators being tested, but only one model was awarded the highest energy saving standard of level 5. • In 1996, the refrigerators’ producers were more ready, EGAT extended the project to cover every model of refrigerator in one-door model. • Two-door model and more than two-door model as well as to cover all brands available in the market. • In 1997, EGAT issued the Act on Energy Saving Label for every one-door model in the market. • In 1998, EGAT required that two-door model refrigerator have the energy saving label for the first time. (...) The next in line of electrical appliances to be promoted in the campaign is air-conditioner as it is one of the home appliances with high growth rate.
Language:English
Score: 1102034.8 - https://www.un.org/esa/sustdev...es/energy/op/clasp_yongppt.pdf
Data Source: un
The proportion of refrigerator sale of one-door model is about 85% and two door model is about 15% 2.! (...) In 1996, the refrigerators’ producers were more ready, EGAT extended the project to cover every model of refrigerator in one-door model. In the same year, EGAT started to expand this campaign to other refrigerator model in the market, which include two-door model and more than two-door model as well as to cover all brands available in the market. In 1997, EGAT issued the Act on Energy Saving Label for every one- door model in the market. In 1998, EGAT required that two-door model refrigerator have the energy saving label for the first time.
Language:English
Score: 1097175.4 - https://www.un.org/esa/sustdev...ssues/energy/op/clasp_yong.pdf
Data Source: un
On Adaptive Neuro-Fuzzy Model Committed to connecting the world Search for: ITU About ITU Media Centre Events Publications Statistics Areas of Action Regional Presence Careers General Secretariat Radiocommunication Standardization Development ITU Telecom Members' Zone Join ITU On Adaptive Neuro-Fuzzy Model Rollup Image You are here ITU > Home > ITU Journal > Issue No.1 > On Adaptive Neuro-Fuzzy Model Share Page Content 10 Title  On Adaptive Neuro-Fuzzy Model For Path Loss Prediction in the VHF Band Abstract Path loss prediction models are essential in the planning of wireless systems, particularly in built-up environments. (...) This paper introduces artificial intelligence in path loss prediction in the VHF band by proposing an adaptive neuro-fuzzy (NF) model. The model uses five-layer optimized NF network based on back propagation gradient descent algorithm and least square errors estimate. (...) The prediction results of the proposed model were compared to those obtained via the widely used empirical models.
Language:English
Score: 1093325.4 - https://www.itu.int/en/journal/001/Pages/08.aspx
Data Source: un
IEC/TC 57 Français   Español   Print Version   Home : ITU-T Home : E-Business : MoU : XML and E-business IEC/TC 57 The scope of this work is the selection of a framework for deregulated electricity market communications. The framework includes a profile of a technical e-business communication architecture for deregulated electricity markets based on ISO/IEC 14662 Information technology – Open-edi reference model and "standardised Internet technologies", notably on XML (Extensible Markup Language) of the W3C (Word Wide Web Consortium) with references to existing or emerging standards or de-facto-standards for global e-business and methods and examples of modelling and messages. (...) Specific aims and reasons The technical standard architecture for electricity markets is more than just an exchange format for data and includes (references B.n.n to the Open-edi reference model ISO/IEC 14662 are given in brackets) a profile with references to existing or emerging Standards of UN/CEFACT, W3C and OMG: A standard mechanism for describing a Business Process and its associated information model (B.2.2). (...) It is expected that, parallel to the standardisation of ebXML as a product standard, UN/CEFACT and OASIS will also standardize horizontal core components of industry business models and of XML message schemas and messages which may be used for the electricity market.
Language:English
Score: 1091827.7 - https://www.itu.int/ITU-T/e-bu...iness/mou/related/iectc57.html
Data Source: un
Microsoft PowerPoint - 02June2021_CAS.pptx Draft results: Modelling Carbon Neutrality - CAS 02 June 2021 ENERGY Modeling Results: CAS The path to carbon neutrality Cumulative mitigation steps from REF to CN (as seen by an observer in 2050) M il li o n t o n n e s o f C O 2 ENERGY Modeling Results: CAS Carbon dioxide emissions CO2 emissions by scenario - CAS Neutrality REF [Million tonnes/year] 2-degree -100 0 100 200 300 400 500 2010 2015 2020 2025 2030 2035 2040 2045 2050 E n e rg y s ys te m C O 2 e m is si o n s [M t/ ye a r] ENERGY Modeling Results: CAS Methane emissions REF Neutrality [Million tonnes/year] CH4 emissions by scenario - CAS 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 2010 2015 2020 2025 2030 2035 2040 2045 2050 E n e rg y s y st e m C H 4 e m is si o n s [M t/ y e a r] 2-degree ENERGY Modeling Results: CAS Sector emissions Carbon emissions by sector 0 50 100 150 200 250 300 350 400 450 2020 2025 2030 2035 2040 2045 2050 0 50 100 150 200 250 300 350 400 450 2020 2025 2030 2035 2040 2045 2050 Reference (REF) Neutrality (CN) Industry Resident/Commercial Transportation Electricity Heat Fuel supply M il li o n t o n n e s C O 2 [M t C O 2 ] M il li o n t o n n e s C O 2 [M t C O 2 ] ENERGY Modeling Results: CAS Electricity Generation Electricity generation by technology - CAS REF Scenario 0 50 100 150 200 250 300 350 400 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal ENERGY Modeling Results: CAS Electricity Generation Electricity generation by technology - CAS CN-UNECE 0 50 100 150 200 250 300 350 400 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal ENERGY Modeling Results: CAS Electricity Generation Electricity generation by technology - CAS CN-UNECE versus REF Scenario - 300 - 200 - 100 0 100 200 300 400 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal ENERGY Modeling Results: CAS The path to carbon neutrality 0 20 40 60 80 100 120 2010 2015 2020 2025 2030 2035 2040 2045 2050 M tC O 2 /y r Biomass CCS Fossil CCS Industrial processes Land use Carbon capture, utilization and storage (sequestration) A mixed set of measures ENERGY Modeling Results: CAS Final Energy Mix Coal Biomass Oil-liquids Bio-liquids Coal-liquids Gas-liquids Gas Hydrogen Elec Heat Sol (el) Other 0.0 0.5 1.0 1.5 2.0 2.5 2010 2015 2020 2025 2030 2035 2040 2045 2050 Fi n a l e n e rg y [ E J] 0.0 0.5 1.0 1.5 2.0 2.5 2010 2015 2020 2025 2030 2035 2040 2045 2050 F in a l e n e rg y [ E J] Final energy mix - Industry Neutrality (CN)Reference (REF) ENERGY Modeling Results: CAS Investment needs Cumulative investments 2020-2050: 1 600 billion US$2020 Reference (REF) Extraction fossil fuel 1,048 Coal 31 Oil Gas 16 Nuclear Hydro 29 Biomass Geothermal Solar 6 Wind 16 T&D 184 Heat supply 3 Hydrogen 3 Other 264 Electricity 98 Extraction fossil fuel Coal Coal CCS Oil Oil CCS Gas Gas CCS Nuclear Hydro Biomass Biomass CCS Geothermal Solar Wind T&D Energy efficiency Heat supply Hydrogen Other ENERGY Modeling Results: CAS Investment needs Cumulative investments 2020-2050: 1 456 billion US$2020 Neutrality (CN) Extraction fossil fuel 528 Coal 30 Coal CCS 1 Oil 0 Gas 19 Gas CCS 5 Nuclear 10 Hydro 112 Biomass 0 Biomass CCS 0 Geothermal 0 Solar 44 Wind 43 T&D 240 Energy efficiency 280 Heat supply 4 Hydrogen 17 Other 123 Electricity 263 Extraction fossil fuel Coal Coal CCS Oil Oil CCS Gas Gas CCS Nuclear Hydro Biomass Biomass CCS Geothermal Solar Wind T&D Energy efficiency Heat supply Hydrogen Other ENERGY Modeling Results: CAS Investment needs Cumulative investment requirements REF, CN and 2-degree  T/D & S: transmission, distribution and storage of electricity and district heat  CCS: carbon capture and storage  BAT: Best available technology 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 Refrence Neutrality 2-degree B il li o n U S $ 2 0 2 0 Energy efficiency & intensity T/D and S Nuclear Renewables (incl. biomass CCS) Hydrogen Fossil CCS Fossil electricity generation ENERGY Modeling Results: CAS Indicators Reference (REF) 0 0.2 0.4 0.6 0.8 1 Energy Intensity (FE) Decline in GDP per capita Energy Expenditure per GDP (FE) Total cost of energy sector per GDP Carbon emissions per GDP Share of non-RE in FE Energy import dependancy Carbon emissions per kWh 2020 2030 2050 ENERGY Modeling Results: CAS Indicators Neutrality (CN) 0 0.2 0.4 0.6 0.8 1 Energy Intensity (FE) Decline in GDP per capita Energy Expenditure per GDP (FE) Total cost of energy sector per GDP Carbon emissions per GDP Share of non-RE in FE Energy import dependancy Carbon emissions per kWh 2020 2030 2050 ENERGY Modeling Results: CAS Impact of different futures 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Final energy Intensity Energy expenditure per GDP Total cost of energy sector per GDP CO2 emissions per GDP Share of non renewables in FE Energy exports per PE Carbon intensity per kWh Reference Neutrality 2-degree Indicators across scenarios (averages between 2020 and 2050)
Language:English
Score: 1091302.3 - https://unece.org/sites/defaul...1_Central%20Asia%20results.pdf
Data Source: un
Microsoft PowerPoint - 02June20212021_SEE-2.pptx Draft results: Modelling Carbon Neutrality - SEE 02 June 2021 ENERGY Modeling Results: SEE The path to carbon neutrality Cumulative mitigation steps from REF to CN (as seen by an observer in 2050) M il li o n t o n n e s o f C O 2 ENERGY Modeling Results: SEE Carbon dioxide emissions CO2 emissions by scenario - SEE Neutrality REF [Million tonnes/year] 2-degree -20 0 20 40 60 80 100 120 2010 2015 2020 2025 2030 2035 2040 2045 2050 E n e rg y s y st e m C O 2 e m is si o n s [M t/ ye a r] ENERGY Modeling Results: SEE Sector emissions Carbon emissions by sector Industry Resident/Commercial Transportation Electricity Heat Fuel supply 0 10 20 30 40 50 60 70 80 90 100 2020 2025 2030 2035 2040 2045 2050 M il li o n t o n n e s C O 2 -20 0 20 40 60 80 100 2020 2025 2030 2035 2040 2045 2050 M il li o n t o n n e s C O 2 Reference (REF) Neutrality (CN) ENERGY Modeling Results: SEE Electricity Generation Electricity generation by technology - SEE REF Scenario 0 20 40 60 80 100 120 140 160 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal ENERGY Modeling Results: SEE Electricity Generation Electricity generation by technology - SEE CN-UNECE 0 20 40 60 80 100 120 140 160 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal ENERGY Modeling Results: SEE Electricity Generation Electricity generation by technology - SEE CN-UNECE versus REF Scenario - 80 - 60 - 40 - 20 0 20 40 60 80 100 120 2010 2015 2020 2025 2030 2035 2040 2045 2050 T W h Wind Offshore Wind Onshore CSP PV Geothermal Biomass CCS Biomass Hydro Nuclear Gas CCS Gas Oil CCS Oil Coal CCS Coal ENERGY Modeling Results: SEE The path to carbon neutrality Carbon capture, utilization and storage (sequestration) A mixed set of measures 0 2 4 6 8 10 12 14 2010 2015 2020 2025 2030 2035 2040 2045 2050 M tC O 2 /y r Biomass CCS Fossil CCS Industrial processes Land use ENERGY Modeling Results: SEE Final Energy Final energy mix [EJ] - Industry Coal Biomass Oil-liquids Bio-liquids Coal-liquids Gas-liquids Gas Hydrogen Elec Heat Sol (el) Other 0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 2010 2015 2020 2025 2030 2035 2040 2045 2050 F in a l e n e rg y, I n d u st ry , R E F 0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4 2010 2015 2020 2025 2030 2035 2040 2045 2050 F in a l e n e rg y, I n d u st ry , N e u tr a li ty Neutrality (CN)Reference (REF) ENERGY Modeling Results: SEE Final Energy Final energy mix – Residential/Commercial Coal Biomass Oil-liquids Bio-liquids Coal-liquids Gas-liquids Gas Hydrogen Elec Heat Sol (el) Other Neutrality (CN)Reference (REF) 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 2010 2015 2020 2025 2030 2035 2040 2045 2050 Fi n a l e n e rg y [ E J] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 2010 2015 2020 2025 2030 2035 2040 2045 2050 Fi n a l e n e rg y [ E J] ENERGY Modeling Results: SEE Investment needs Extraction fossil fuel 7.5 Coal 1.6 Coal CCS 0.3 Oil 1.4 Gas 0.5 Gas CCS 0.4 Nuclear 13.7 Hydro 54.6 Biomass 0.9 Biomass CCS 0.0 Geothermal 0.0 Solar 7.4 Wind 18.2 T&D 77.4 Energy efficiency 34.4 Heat supply 1.6 Hydrogen 1.1 Other 8.9 Electricity 99.2 Extraction fossil fuel Coal Coal CCS Oil Oil CCS Gas Gas CCS Nuclear Hydro Biomass Biomass CCS Geothermal Solar Wind T&D Energy efficiency Heat supply Hydrogen Other Cumulative investments 2020-2050: 129.0 billion US$2020 Reference (REF) ENERGY Modeling Results: SEE Investment needs Extraction fossil fuel 16.9 Coal 14.7 Oil 0.1 Gas 0.1 Hydro 19.2 Biomass 0.0 Geothermal 0.0 Solar 7.2 Wind 6.8 T&D 56.2 Heat supply 0.6 Hydrogen 2.5 Other 0.0 Electricity 48.2 Extraction fossil fuel Coal Coal CCS Oil Oil CCS Gas Gas CCS Nuclear Hydro Biomass Biomass CCS Geothermal Solar Wind T&D Energy efficiency Heat supply Hydrogen Other Cumulative investments 2020-2050: 230.1 billion US$2020 Neutrality (CN) ENERGY Modeling Results: SEE Investment needs Cumulative investment requirements REF, CN and 2-degree 0 50 100 150 200 250 Reference Neutrality 2-degree B il li o n U S $ 2 0 2 0 Energy efficiency & intensity T/D and S Nuclear Renewables (incl. biomass CCS) Hydrogen Fossil CCS Fossil electricity generation Fossil fuel (extraction, transmission, and processing)  T/D & S: transmission, distribution and storage of electricity and district heat  CCS: carbon capture and storage  BAT: Best available technology ENERGY Modeling Results: SEE Indicators Comparing different indicators relative to 2020, Reference scenario 0 0.2 0.4 0.6 0.8 1 Energy Intensity (FE) Decline in GDP per capita Energy Expenditure per GDP (FE) Total cost of energy sector per GDP Carbon emissions per GDP Share of non-RE in FE Energy import dependency Carbon emissions per kWh 2020 2030 2050 ENERGY Modeling Results: SEE Final Energy Mix Comparing different indicators relative to 2020, Neutrality scenario 0 0.2 0.4 0.6 0.8 1 Energy Intensity (FE) Decline in GDP per capita Energy Expenditure per GDP (FE) Total cost of energy sector per GDP Carbon emissions per GDP Share of non-RE in FE Energy import dependency Carbon emissions per kWh 2020 2030 2050 ENERGY Modeling Results: SEE Impacts of different futures Indicators across scenarios (averages between 2020 and 2050) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Final energy Intensity Energy expenditure per GDP Total cost of energy sector per GDP CO2 emissions per GDP Share of non renewables in FE Energy exports per PE Carbon intensity per kWh Reference Neutrality 2-degree
Language:English
Score: 1086904.1 - https://unece.org/sites/defaul...%20East%20Europe%20results.pdf
Data Source: un
Technological advancements coupled with the decline in solar energy costs and increased local expertise need to be capitalized on in order to further advance the use and security and sustainability of renewable energy sources. Testing new models of future investment is worth exploring – models that can contribute towards bridging the electricity gap and addressing the security issue while assisting the State of Palestine in meeting its global commitment towards reducing greenhouse gas emissions. (...) Such a model would ultimately contribute towards matching the supply with the demand and tackling the electricity deficit, which seriously affects livelihoods in the Gaza Strip and sustainable development at large. (...) This solution would provide a model for scattered electricity generation. UNDP will continue its efforts to keep the sustainable-energy agenda for the Palestinian people as its priority and will increase its advocacy and lobbying efforts to test new solutions even as it increases investment in solar energy through its various development interventions.
Language:English
Score: 1085224.7 - https://www.undp.org/papp/blog...-energy-sector-state-palestine
Data Source: un
Page 5 - 2016 Integrated management and disposal of electrical and electronic waste and used electrical and electronic equipment in Latin America           Basic HTML Version Table of Contents View Full Version Page 5 - 2016 Integrated management and disposal of electrical and electronic waste and used electrical and electronic equipment in Latin America P. 5 Table of contents Page Executive summary ............................................................................................................................... iii Chapter 1 – Introduction and objectives of the ITU-T Study Group 3 (SG3) cost model project .................. 1 1.1 Introduction – Background and context .............................................................................................1 1.2 The supply side begins to react against roaming................................................................................2 1.3 The consumer is becoming more and more conscious of the penalties of roaming, raising a key question ..........................................................................................................................................................4 1.4 What deliverables are required? ........................................................................................................4 Chapter 2 – Principles of telecommunications costing and the basis of the cost model ............................. 5 2.1 Principles of telecommunications costing ..........................................................................................5 2.2 The key principles for the roaming model ..........................................................................................6 Chapter 3 – Benefits and limits of the cost model .................................................................................. 11 3.1 Benefits ............................................................................................................................................ 11 3.2 Limits of the model .......................................................................................................................... 12 3.3 Further cost items that may be included to extend the model, when significant .......................... 13 Chapter 4 – Methodology: The cost model, with its mechanisms and assumptions ................................ 16 4.1 Methodology ................................................................................................................................... 16 4.2 Mechanisms for calculation – Business process analysis ................................................................ 20 4.3 Going from use cases to analysis of assets used in roaming to their costs ..................................... 25 Chapter 5 – Using the cost model: Guidelines for NRAs, with data collection.......................................... 33 5.1 How to use the cost model .............................................................................................................. 34 5.2 Effective data gathering – Data collection process and interaction with MNOs ............................. 35 5.3 Sample questionnaire for NRAs, for use with the cost model ......................................................... 38 Annex 1 – The regulatory situation in the EU ......................................................................................... 39 Annex 2 – User startup – A one-page summary ..................................................................................... 46 Annex 3 – A model questionnaire for gathering roaming data ................................................................ 47 Annex 4 – Example of spreadsheet used to gather data ......................................................................... 51 Annex 5 – Example of parameters used to calculate MNO costs of roaming ........................................... 52 Abbreviations ...................................................................................................................................... 55 List of references, background, and further reading ............................................................................... 59 i     1     2     3     4     5     6     7     8     9     10          
Language:English
Score: 1084005.1 - https://www.itu.int/wftp3/Publ...As/files/basic-html/page5.html
Data Source: un
MODELING TOOLS – INTEGRATED APPROACHES FOR SUSTAINABLE DEVELOPMENT Workshop for UN staff and policymakers 22-26 August 2016, Addis Ababa 1 AGENDA Monday 22 August 2016 08:30 09:00 Registration of Participants 09:00 09:20 Welcome Remarks  Gerd Trogemann, Director, Regional Service Center, UNDP, Addis  Diana Alarcon, Chief, Development Strategy and Policy Analysis , UNDESA, NY   Mark Howells, Director, Division of Energy Systems Analysis, KTH Overview of the week program and introductions 09:20 09:40 The tasks for the week, Eduardo Zepeda (EZ) 09:40 10:20 Round of Introductions, Caroline Lensing-Hebben (CL) 10:20 10:40 Coffee break TRAINING MODULE 1: Sustainable Development, SDGs and modeling Modelling Sustainable Development: the Global Approach Moderator: Caroline Lensing-Hebben (CL) 10:40 12:00 M 1.1 Modelling Tools for Sustainable Development & 2030 Agenda, (EZ) 12:00 13:00 M1.2 Climate, Land, Energy and Water: A Global Model, Mark Howells (MH) 13:00 14:30 Lunch break 14:30 15:30 M1.2 Climate, Land, Energy & Water: A Global Model: Hands-On, Thomas Alfstad (TA) 15:30 15:50 Coffee break Modelling Sustainable Development: briefs on country approaches Moderator: (CL) 15:50 16:02 M1.M2 Climate, Land, Energy and Water: Country Model (TA) 16:02 16:14 M1.M3 Energy Systems Modelling in Countries, MH & Chris Arderne (CA) 16:14 16:26 M1.M4 Modelling Electricity for All, MH and Dimitris Mentis (DM) 16:26 16:38 M1.M5 Microsimulation for Social Inclusion (EZ) 16:38 17:10 SDGs and Modelling Tools: Open Discussion (TA) 17:10 Adjourn MODELING TOOLS – INTEGRATED APPROACHES FOR SUSTAINABLE DEVELOPMENT Workshop for UN staff and policymakers 22-26 August 2016, Addis Ababa 2 Tuesday 23 August 2016 TRAINING MODULE 2: SDGs, COUNTRY INTEGRATED ASSESSMENT 09:00 09:50 M2.1 Integrated Assessment: Sustainable development & the food-energy-water nexus Resources: (TA, MH, CA) Moderator: (EZ) 09:50 09:55 5 minute break 09:55 10:45 M2.2 The Climate Land Energy and Water Systems (CLEWS) approach 10:45 11:00 Coffee break 11:00 12:30 M2.3 CLEWS country case studies 12:30 14:00 Lunch break 14:00 15:30 M2.4 Hands on experience with CLEWS 15:30 15:50 Coffee break 15:50 17:00 M2.4 Hands on experience with CLEWS, continued 17:00 17:10 M2.6 Wrap-up and assignments 17:10 Adjourn Wednesday, 24 August 2016 TRAINING MODULE 3: SUSTAINABLE ENERGY 09:00 09:50 M3.1 Energy and Development Resources: (CA, MH, TA) Moderator: (EZ) 09:50 09:55 5 minute break 09:55 10:45 M3.2 Energy Policy 10:45 11:00 Coffee break 11:00 12:30 M3.3 Energy Systems modelling 12:30 14:00 Lunch break 14:00 15:30 M3.4 OSeMOSYS and MoManI 15:30 16:00 Coffee break 16:00 16:30 M3.5 Modelling Tools & Capacity Development: Bolivia, Marcelo Velazquez, Bolivia Gov. 16:30 17:50 M3.6 Electricity systems modelling: hands on experience 17:50 18:00 M3.7 Wrap-up and assignments 18:00 18:00 Adjourn MODELING TOOLS – INTEGRATED APPROACHES FOR SUSTAINABLE DEVELOPMENT Workshop for UN staff and policymakers 22-26 August 2016, Addis Ababa 3 Thursday, 25 August 2016 TRAINING MODULE 4: UNIVERSAL ACCESS TO ELECTRICITY 09:00 09:50 M4.1 Assessing Energy and Electricity for All using GIS Resources: (DM, MH, CA) Moderator: (TA) 09:50 09:55 5 minute break 09:55 10:45 M4.2 Introduction to the Open Source Spatial Electrification Toolkit 10:45 11:00 Coffee break 11:00 12:30 M4.3 Electrification analysis using GIS 12:30 12:50 M3.4 Modelling Tools & Capacity Development: Uganda, Tasha Balunywa, Uganda Gov. 12:50 13:50 Lunch break 13:50 15:20 M4.5 The Online Electrification Interface 15:20 15:50 Coffee break 15:50 17:10 M4.6 Hands on experience with the electrification model (ONSET) 17:10 17:40 M4.7 Presenting models to Governments for SDGs policies, Experts & Policy Advisers 17:40 Adjourn Friday, 26 August 2016 TRAINING MODULE 5: SOCIAL INCLUSION 09:00 09:40 M5.1 Social inclusion and microsimulation: Growth and Social Inclusion, Bolivia Resources: (EZ, TA, DM) Moderator: (MH) 09:40 10:40 M5.2 Microsimulation: Growth and Social Inclusion, Bolivia: Hands-On 10:40 11:00 M5.3 Modelling Tools & Capacity Development: Nicaragua, Osmar Cuadra, Nicaragua Govt. 11:00 11:20 Coffee break 11:20 13:10 M5.4 Estimating electricity demand and sensitivity to inequality 13:10 14:10 Lunch break 14:10 16:00 M5.4 Estimating electricity demand and sensitivity to inequality: Hands-On, cont. 16:00 16:20 Coffee break 16:20 18:00 RECAP OF WORKSHOP 18:00 Adjourn
Language:English
Score: 1078673.6 - https://www.un.org/development...blication/2016_UNDP-agenda.pdf
Data Source: un