Changes In Energy Use Research Articles

A geographical review of urban farming and urban heat island in developing countries

Cities in developing countries are heavily involved in urban agricultural activities, and a lot of research has been done to address the challenges in this regard. However, several studies have proposed taking advantage of them in reducing the impact of urban heat islands (UHIs), which are manifested as a consequence of stringent changes in energy use, human settlement patterns, industry, transportation, and population growth. Data for this study were obtained from a literature search of existing academic journals, the domain of agriculturally related organisations, and books; the investigation was mainly based on developing nations. Best practices of scholarly work did the review. Authors stand posited. Urban farming has been employed in cities mainly as a food source while also catering to the changing needs of the urban dweller’s lifestyles. There is an urgent need to investigate the potentials of urban farming not just as food crops but as green infrastructure in ameliorating urban heat island intensities in our cities.

Carbon monoxide and nitrogen dioxide patterns associated with changes in energy use during the COVID-19 pandemic in Kenya

<abstract> <p>Environmental degradation, including air quality deterioration, has been mainly attributed to anthropogenic activities. Air pollution has become a pressing issue in industrialised and highly populated areas due to the combustion of fossil fuels and industrial operations. Recently, the COVID-19 pandemic led to a nationwide lockdown to control the spread of the coronavirus. This imposed restrictions on many economic activities, thus providing the environment with an opportunity to heal. The COVID-19 response measures adopted by most countries, including lockdown, restricted movement, and other containment measures, led to a significant decrease in energy use in the transport sector. Due to low electricity access levels in developing countries, traditional energy sources make up the bulk of energy used for most domestic energy services. Biomass combustion emits carbon monoxide (CO), while the transport sector is a major contributor of nitrogen dioxide (NO<sub>2</sub>). This study was purposed to investigate the short-term effects of COVID-19 on CO and NO<sub>2</sub> concentration levels in Nairobi, Vihiga and Tana River counties. The study utilised data on CO surface concentration, NO<sub>2</sub> column concentration and reported COVID-19 cases. Time series, correlation analysis and spatial and temporal map analysis were carried out to investigate the changes and relationships among the study parameters. The three counties were selected based on the urbanisation and population. Nairobi county represented an urban setting, while the Vihiga and Tana River counties represented rural areas with high and low population densities, respectively. The CO surface concentrations in Nairobi and Vihiga county significantly correlated with the COVID-19 cases, with both counties portraying negative correlations, i.e., −0.59 (<italic>P</italic>-value: 0.008) and −0.45 (<italic>P</italic>-value: 0.05), respectively. NO<sub>2</sub> column concentration also exhibited a significant negative relationship with reported COVID-19 cases in the Vihiga (−0.018, <italic>P</italic>-value = 0.05) and Tana River (0.17, <italic>P</italic>-value = 0.00) counties. These findings highlight the need for demographic and economic considerations in CO and NO<sub>2</sub> assessments, and allude to a decreased health risk due to CO and NO<sub>2</sub> emissions during the COVID-19 pandemic.</p> </abstract>

Short and Long-Run Causal Effects of CO2 Emissions, Energy Use, GDP and Population Growth: Evidence from India Using the ARDL and VECM Approaches

This paper investigates the nexus between CO2 emissions (CO2E), GDP, energy use (ENU), and population growth (PG) in India from 1980–2018 by comparing the “vector error correction” model (VECM) and “auto regressive distributed lag” (ARDL). We applied the unit root test, Johansen multi-variate cointegration, and performed a Variance decomposition analysis using the Cholesky approach. The VECM and ARDL-bound testing approaches to cointegration suggest a long-term equilibrium nexus between GDP, energy use, population growth and CO2E. The empirical outcomes show the existence of a long-term equilibrium nexus between the variables. The Granger causality results show that short-term bi-directional causality exists between GDP and ENU, while a uni-directional causality between CO2E and GDP, CO2E and ENU, CO2E and PG, and PG and ENU. Evidence from variance decomposition indicates that 58.4% of the future fluctuations in CO2E are due to changes in ENU, 2.8% of the future fluctuations are due to changes in GDP, and 0.43% of the future fluctuations are due to changes in PG. Finally, the ARDL test results indicate that a 1% increase in PG will lead to a 1.4% increase in CO2E. Our paper addresses some important policy implications.

Social practices and energy use at home during the first Italian lockdown due to Covid-19.

During the beginning of the Covid-19 pandemic, cities became large residential consumers of energy. In general, energy demand has decreased, but the users who used the most energy during the pandemic were the people in their homes creating a change compared to the past. How have household habits changed affecting energy use during the lockdown? Has energy demand changed equally in all homes? What factors help explain the change in daily household habits and the change in energy use? Via distribution of a questionnaire completed by 3519 people living in Italy during the first lockdown #StayAtHome, the change in daily habits and consequent energy use were investigated. It collected data on socio-demographic and household characteristics and the material context in which people live. The results were interpreted according to the social practice approach that has been used in the past to analyse energy habits and use of households, for example, for cooking. The results can support the interpretation of energy demand studies in the pandemic period and address decisions and policymaking for sustainable energy transition.

Energy use and the role of per capita income on carbon emissions in African countries.

This study contributes towards the realization of Sustainable Development Goal (SDG) 13 which aims “take urgent action to combat climate change and its impacts” by investigating the role of per capita income in moderating the impact of energy use on carbon emissions. Using data from 28 selected African countries covering 1990 to 2019 and deploying the FGLS, PCSE, and MM-QR techniques, findings reveal, among others, that: at the 1% significance level, a percentage change in energy use leads to between 0.60% and 0.70% increase in carbon emissions, on average, ceteris paribus. Correspondingly, income shows to be a positive driver of emissions contributing between 0.87% and 0.84% percentage increase, on average, ceteris paribus. Also, per capita income attenuates the impact of energy use on emissions by between -0.27% and -0.23%, on average, ceteris paribus. However, significant heterogeneities occur across the sub-regions. Specifically, Southern Africa shows the largest energy contributor to emissions 1.65% while Central Africa contributes the most to aggravating emissions by 1.87% through increase in per capita income. West Africa shows the largest moderation effect at -0.56%. Across the quartiles, the effects of energy use and per capita are positive. Given these, we submit that the strong correlation between energy usage and per capita income (i.e. economic growth) poses a dilemma for African economies in their drive for growth. Leaving room for trade-offs. Perhaps, the lesson is that as African countries seek for more development without contributing to carbon emissions, governments should invest more in renewable energy.

Retrofit effectiveness: Evidence from a nationwide residential energy efficiency programme

Buildings in the EU are responsible for 40% of energy use, with approximately 75% of buildings classed as energy inefficient. Many energy efficiency technologies have failed to deliver on the promised savings. This paper measures the extent to which residential dwelling retrofit delivers real energy savings in Ireland using a unique dataset of whole-home energy use (n = 8,572 households) over a two-year period. It is one of the largest studies of retrofit using whole-home energy data for a general housing sample, who do not disproportionately experience fuel poverty. It uses whole-home energy data to capture potential fuel switching induced by retrofit. Finally, this study addresses concerns regarding self-selection issues associated with the decision to undergo a retrofit by comparing with a second control group of homes that received a retrofit prior to the observation period.Results following a generalised difference-in-difference methodology indicate that retrofits reduce energy use by 1,091 kWh/year, on average. However, the magnitude of savings depends on the measures installed. Further analysis suggests that retrofits represent better value for money when factoring in actual changes in energy use. This study highlights possible unintended consequences of retrofit policies that may hinder progress towards national energy efficiency targets.

Promoting Sustainable and Healthy Diets to Mitigate Food Insecurity Amidst Economic and Health Crises in Lebanon.

Introduction: Lebanon, a middle-income Eastern Mediterranean country, continues to face detrimental economic, health and socio-political challenges that are further exacerbated by the COVID-19 pandemic. In parallel, the country has been experiencing a remarkable nutrition transition that has contributed to the burden of malnutrition and non-communicable diseases, all imposing serious repercussions on people's livelihoods, food security, and health. Such circumstances have prodded public demand for guidance on affordable, healthy, and sustainable dietary choices to alleviate the burden to this emerging unfortunate situation.Objective: The purpose of this study is to provide evidence-based sustainable and healthy dietary recommendations which balance the tradeoffs among the health, environmental footprint and cost dimensions of sustainability, while closely resembling the usual food consumption pattern.Methodology: Data from the latest available national food consumption survey was used as the usual food consumption pattern of Lebanese adults. Optimized dietary patterns were calculated using the optimization model Optimeal which produced patterns most similar to the usual diet and simultaneously satisfying the three main sets of constraints: health, environmental footprints, and cost. The identified healthy and sustainable dietary options were vetted by multiple key stakeholders from the government, academia, international, and national non-governmental organizations.Results: Compared to the usual intake, the optimized diet included higher intakes of whole grain bread, dark green vegetables, dairy products, and legumes, and lower intakes of refined bread, meat, poultry, added sugars, saturated fat, as compared to usual national mean consumption. The optimized dietary model resulted in a decrease in the associated environmental footprints: water use (−6%); and GHG (−22%) with no change in energy use. The cost of the optimized diet was not different from that of the usual intake.Conclusion: An evidence-based sustainable and healthy diet was developed for Lebanon providing the population and policy makers with some answers to a complex situation. Findings highlight the need for the development of sustainable food based dietary guidelines for Lebanon to promote diets that are healthy, sustainable, culturally acceptable, and affordable and that can alleviate food insecurity among the general population.

The impact of off-grid solar home systems in Kenya on energy consumption and expenditures

This paper assesses the impact of solar home systems (SHS) on energy consumption and energy-related expenditures among Kenyan households. Based on a pipeline comparison approach of more than a thousand households, we find that access to a SHS leads to a net increase of 24 to 36 min in daily lighting use due to a 3 h increase in the use of LED lamps, accompanied by a reduction in the use of “dirty” lamps. We also find a one litre reduction in the monthly use of kerosene for lighting. These changes in energy use along with a decline in the cost of charging mobile phones leads to a reduction in overall monthly expenditure of KSh 193 (about EUR 1.60). For the most popular SHS, these economic effects translate into a payback period of about six years. In addition to the economic benefits, we find small positive environmental effects, increased satisfaction from better quality lighting and an increase in time spent watching TV. Setting the costs against the combined benefits of SHS suggests a positive payoff for this particular off-grid electricity option and a viable way forward in providing off-grid energy solutions with a wide range of functionalities in developing countries.

Energy optimization in wheat establishment following rice residue management with Happy Seeder technology for reduced carbon footprints in north-western India

Rice residue management (RRM) impacts ecosystems’ sustainability due to change in energy use, C footprints (CFs) and net ecosystem C budget (NECB). The energy input-output and CFs were audited to optimize energy use and C equivalent emissions (CE) in wheat following RRM with Happy Seeder using data collected from 53 farmers during face-to-face interview. Average total energy input (EI) of 23.5 GJ ha−1 produced output energy of 204.8 ± 2.9 GJ ha−1, with energy productivity (EP) of 0.24 kg MJ−1. The net energy gain (NEG) of 181.3 ± 3.0 GJ ha−1 was achieved with energy use efficiency (ER) of 8.81 ± 0.19. Chemical fertilizers were the major hotspots of energy (∼48.4% of EI) and CFs (∼59.2% of CE) in wheat cultivation. Data Envelopment Analysis (DEA) elucidated 23 decision making units (∼43% of total) as energy efficient, whilst 30 as inefficient with average technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were 0.94, 0.97 and 0.94, respectively. DEA helped energy saving of 1462.8 MJ ha−1 (∼6.2%), mitigation of 107.8 kg CO2e ha−1 (∼7.6%) with significantly higher NECB of 716.4 kg C ha−1 (∼19.1%) due to change in soil organic C (ΔSOC) pool by 34.8 kg C ha−1 at efficient Happy Seeder sown wheat farms.

Investigating the Application of a Transportation Energy Consumption Prediction Model for Urban Planning Scenarios in Machine Learning and Shapley Additive Explanations Method

Accurate forecasts of future energy usage are an important step towards reaching carbon mitigation commitments for city policymakers. Beyond identifying sources of emission intensity for a region, the forecast mechanism must be capable of compensating for gaps in available data and of accounting for the uncertainties behind the dynamics of an urban system. By considering a range of possible scenarios, the prediction model can identify recurring sources of high energy consumption and fine-tune areas of priority with incoming data. This paper considers the impact of predicted shifts in demographic and economic trends for the region on transportation energy consumption. The transportation energy use model is formulated from the Delaware Valley Regional Planning Commission (DVRPC) open-source Household Travel Survey (HTS). Based on these data inputs, a Machine Learning (ML) algorithm is implemented in the form of an Extreme Gradient Boosting (XGBoost) model to estimate energy consumption with a corresponding SHapley Additive exPlanations (SHAP) analysis of feature contribution. From this, a synthetic population is produced using the ML outputs and marginal sums with data from the Census Bureau’s American Community Survey (ACS) to estimate energy consumption for the region. The results indicate that shifting dominant travel modes and income distribution in accordance with the Enduring Urbanism forecast projections led to a decrease in household transportation energy use. Moreover, additional analysis of the model output demonstrates that changes in energy use depend strongly on geographic area and income group.

Water-Cooled Chiller Terintegrasi Heat Recovery System pada Industri Perhotelan di Bali

Performance testing of water-cooled chiller integrated with a heat recovery system has been carried out at a hotel in Bali. After commissioning, testing is carried out for 2 days to determine the readiness of the components in operation. The coefficient of performance which is the reference in this study is used to determine the performance of the system with a capacity of 275 tons of refrigeration which is divided into the coefficient of performance of the refrigeration system and the coefficient of performance of the total integrated system. Data was collected with 8 hour intervals starting at 06.00, 14.00, and 22.00 Wita every day. This condition is based on the prediction of the cycle of changes in energy use in the hotel, which includes breakfast, hotel check-in and bedtime. From the research results, it was found that with the condition and situation of the hotel were not measured properly and the occupancy rate of the hotel which was not yet maximal resulted in a greater refrigeration capacity on the second day, but this was also followed by a large demand for compressor power. Although in the end the coefficient of performance was slightly larger than day 1. It was also found that the greatest need for heat transfer rate is required at 22.00 Wita during the 2-day test. This is possibly due to the need for hot water for hotel guests to shower at night. In general, an increase in the coefficient of performance is still possible if there is an increase in hotel occupancy rates.

Drivers behind Residential Electricity Demand Fluctuations Due to COVID-19 Restrictions

The COVID-19 pandemic rapidly reoriented the lives of billions of people across the globe toward working, learning, and subsisting from home. This paper examines the consequences of this disruption of electricity use in Australian households. Using high-frequency electricity monitoring from 491 houses and per-circuit monitoring and in-depth interviews with 17 households, the paper (1) compares changes in energy use before and during COVID-19 lockdown, (2) quantifies the key drivers of changes in energy use experienced by households during lockdown, and (3) tracks households’ interactions with energy use feedback. The findings identify significant increases in certain aspects of household electricity use directly related to COVID-19, including increased cooking and digital device use. Yet despite the government mandate requiring a large proportion of the population to remain at home, overall energy use among the majority of Queensland households monitored actually decreased during lockdown versus prior, driven primarily by a reduction in air conditioner use during lockdown as the weather cooled. Further, despite significant quantified and self-reported changes in energy use, users who had energy use feedback installed accessed their dashboards less during lockdown than they did prior. The paper discusses these results in the context of statistics on COVID-19 related energy demand fluctuations elsewhere, and the implications for the provision of energy use information to residents during significant disruptions such as lockdown.

Gedragseconomische valkuilen bij de energietransitie

Abstract Behavioral-economic pitfalls in the energy transitionThe energy transition requires large investments. The public debate in the Netherlands focuses on the associated costs, and the government uses traditional instruments such as subsidies. From the perspective of behavioral economics this approach has a few drawbacks. The goal of this article is to discuss behavioral-economic pitfalls which are important for two topics regarding the energy transition. First, households need to take measures themselves with regard to the switch to alternative energy sources. Several behavior-economic pitfalls influence households’ decisions. The most important ones are risk aversion, loss aversion, choice overload and motivation crowding out. A number of measures are discussed which the government might employ to facilitate this behavioral change by households. Second, large investments will be needed in the electricity network due to the energy transition. These costs might be mitigated by a behavioral change in energy use of households and firms: either by using less energy or by using energy at other times of the day. This might be achieved by introducing dynamic electricity tariffs. However, behavioral-economic pitfalls will affect the interest in such tariffs, most notably motivation crowding out and moral licensing. Their effects will be lessened if financial incentives are not emphasized.

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. We use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

Sources and historical sedimentary record: Temporal variability of n-alkane and PAHs from the Yellow River Estuary, China

The composition and distribution of n-alkanes and polycyclic aromatic hydrocarbons (PAHs) in suspended particulate matter (SPM) from the Yellow River and a sedimentary core from the Yellow River Estuary, China, were measured in order to determine environmental changes in the Yellow River Estuary on a regional scale. The concentration of n-alkane in SPM increased along the upper-middle-lower reaches of the Yellow River. The total concentrations of n-alkanes and PAHs in the core ranged from 0.04 to 0.86 μg g−1 (avg. 0.21 μg g−1), and from 0.04 to 0.29 μg g−1 (avg. 0.15 μg g−1) on a dry wt. basis, respectively. Understanding the temporal evolution of n-alkanes provide information on terrigenous versus aquatic productivity, oil exploration at the Shengli Oilfield, and channel diversion in the Yellow River. n-Alkanes in SPM were mainly derived from mixed sources, with terrigenous inputs dominating. PAHs in the sediment core were predominantly derived from coal and biomass combustion. The variation in PAHs levels throughout the core determines changes in energy use and socio-economic development. The temporal variability in n-alkane and PAHs and their molecular diagnostic ratios revealed a trend of regional environmental change and the role of anthropogenic activity in that environmental change.

Do CO2 emissions impact energy use? An assessment of China evidence from 1953 to 2017

Bidirectional causality between energy use and carbon emissions has widely been explored, but the results in the existing research are still controversial. Focusing on China, we reveal the bidirectional causality between energy use and carbon emissions, and then take carbon emissions as a factor influencing energy use into decomposition analysis by the Logarithmic Mean Index (LMDI) approach. The study span 1953–2017 is divided into two main developing stages based on multiple breakpoint tests, stage one: 1953–2001, before entering World Trade Organization (WTO); and stage two: 2001–2017, after entering WTO. The results show that emission effect and per capita emission effect indeed promoted energy use and even surpassed output effect at the beginning of stage two (2001–2005). The changes in energy use followed those in output effect at stage one, emission effect and per capita emission effect at the beginning of stage two, and energy intensity effect at the latter period of stage two. It indicates that energy efficiency has gradually been becoming a dominate role in energy use changes. The impact of energy mix effect was comparatively small.

The metabolic alteration and apparent preservation of the zombie ant brain

Some parasites can manipulate the behavior of their animal hosts to increase transmission. An interesting area of research is understanding how host neurobiology is manipulated by microbes to the point of displaying such aberrant behaviors. Here, we characterize the metabolic profile of the brain of an insect at the moment of the behavioral manipulation by a parasitic microbe. Our model system are ants infected with the parasitic fungus Ophiocordyceps kimflemingiae (=unilateralis), which manipulates ants to climb and bite into plant substrates, before killing the host (i.e. zombie ants). At the moment of the behavioral manipulation by the fungus, the host’s brain is not invaded by the fungus which is known to extensively invade muscle tissue. We found that, despite not being invaded by the parasite, the brains of manipulated ants are notably different, showing alterations in neuromodulatory substances, signs of neurodegeneration, changes in energy use, and antioxidant compound that signal stress reactions by the host. Ergothionine, a fungal derived compound with known neuronal cytoprotection functions was found to be highly elevated in zombie ant brains suggesting the fungus, which does not invade the central nervous system, is preserving the brain.

Implications of biogas and electric cooking technologies in residential sector in Nepal – A long term perspective using AIM/Enduse model

This paper aims to analyze the effects of biogas and electricity based cooking on energy use and greenhouse gas (GHG) as well as local air pollutant emissions during 2010–2050 in the case of Nepal, which is highly dependent on traditional biomass (mainly fuelwood) for cooking. The country is rich in hydropower resources. A long-term bottom-up energy system model has been developed using Asia-Pacific Integrated Model/Enduse (AIM/Enduse) model for the analysis. The study developed a business as usual (BAU) scenario and three alternative cooking scenarios. Three alternative scenarios, named as “CL”, “CM” and “CH” scenarios; consider low, medium and high level of penetrations of electric- and biogas-based cooking options, respectively. The changes in energy use and electricity generation in the BAU and alternative scenarios have been compared. Fuelwood consumption in the residential sector in 2050 when compared to the BAU would decrease by 12.5% in CL, 19.0% in CM and 24.2% in CH scenarios; and liquefied petroleum gas (LPG) consumption would decrease by 12.8% in CL, 16.3% in CM and 19.6% in CH scenarios. The electricity generation requirement in 2050 would increase by 9.4% in CL, 13.9% in CM and 17.0% in CH scenarios. Finally, the assessment of GHG and local pollutant emissions shows the decrease in all gases in CL, CM and CH scenarios when compared to the BAU.

Residential energy transition and thermal efficiency in an arid environment of northeast Patagonia, Argentina

We have studied changes in energy use and practices due to a program which partially replaced firewood by fossil fuels. The program has been running for nearly five years in rural dwellings in Patagonia Argentina, where limited provision of fuelwood and social vulnerability are found. As a case study we focused on the community of Laguna Blanca, Province of Río Negro, located in the Patagonian steppe. Dwellings are one-family houses which were built with no energy efficiency measures, exposing reinforced concrete structures, fired-clay bricks, metal roofs and single-glass windows to severe weather. Since 2014, a government program fully subsidized the refilling of Liquid Petroleum Gas (LPG) to vulnerable rural communities, which were provided with a 200-kg gas tank and a gas heater in each household. An expected decrease in the use of fuelwood was observed, along with a very high consumption of LPG (around 650 kWh/m2 year), which can be for the first time assessed due to the known amount of LPG provided. The lack of energy efficiency policies led to very high operational costs and environmental impacts. Based on the dwellings' characteristics, we have also found that the LPG subsidies saved in 2.2 years would return the investment for a thermal insulation that would reduce 50% the gas consumption and substantially improve comfort.

Measuring energy efficiency - structural and index decomposition analysis

Aim: The aim is to recognize the main determinants of the energy efficiency improvement in transport in Poland in the years 2000-2014 using structural and index decomposition analysis, and to identify areas where there is still potential for further reduction of energy consumption.Design / Research methods: Techniques used to analyse changes in energy use are: structural decomposition analysis and index decomposition analysis. Each of these two methods is characterized by distinctive, unique techniques and approaches, as they have developed quite independently. Index decomposition analysis measures the impact of energy efficiency gains on the level of energy consumption, at the most detailed sector disaggregation level allowed by the available data. Whereas structural decomposition analysis allows to analyse the impact of the external factors, such as technological, demand, and demographic effects, on the fluctuations of the total energy consumption. The similarities and differences between the two approaches are summarized and illustrated with a numerical example of Polish transport.Conclusions / findings: The article recognizes the main determinants of the energy efficiency improvement in transport sector in Poland in the years 2000-2014. In case of Poland ODEX shows an overall progress of energy efficiency in transport by 24.3%. Results obtained with decomposition analysis indicate large divergences in energy efficiency improvements between modes of transport and vehicle types and identify areas where there is still potential for further reduction of energy consumption. Results from decomposing structure of energy use, show activity effect to be main reason for energy use growth. The distribution of each mode in total traffic of passengers and goods changes toward less energy efficient modes. The only factor driving down the energy use is energy savings.Originality / value of the article: Using two methods of decomposition analysis and comparing obtained outcomes allows to get a broader view on energy use trends. Results presented in this article are a good starting point for further detailed analysis of changes in energy use of transport.