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Blog: Productivity Pathways for Meeting Farming Demand Sustainably

Matheus Mansour is a final-year undergraduate student in Industrial Engineering at the University of Sao Paulo’s Polytechnic School. Matheus is from Brazil and is interested in statistics, operations research, machine learning and tech businesses in general. He is currently working on his capstone project where he applies neural networks to build a forecasting model for farming production in Brazil. In this blog, Matheus writes about this project and explains how its methodology can be used as a step to guide public policy towards a more sustainable future worldwide. 


Much has been said about sustainability over the past 30 years. Starting from the basic definition of satisfying the needs of the present without compromising the capacity of future generations of satisfying their own needs, there are many aspects that must be taken care of to ensure an overall positive outlook for the generations to come.

One such aspect concerns taking action to combat climate change and its impacts. It is known that the current climate change is mainly caused by human activity (i.e. by people burning fossil fuels and converting land from forests to agriculture, thus releasing carbon dioxide into the atmosphere). Regarding the latter, the incentives for such behaviour are plentiful: with an ever-growing population and limited land supply, natural coverage areas are being deforested in order to grow crops and meet the consequential rising farming demand. Specifically in Brazil, for instance, it is estimated (FAO) that 20% of the Amazon rainforest has been lost to deforestation over the past 50 years.


In addition, more than two thirds of the national gross CO2 emissions come from land use, land-use change and forestry (FILHO et al., 2010). As carbon dioxide is one of the main drivers of climate change, an appropriate national-level set of public policies to avoid deforestation is thus expected to bring high dividends. This has to be done, however, while still allowing the productive sector to meet agricultural and livestock demand of an expanding economy so as to not harm the country’s development.

If deforestation is to be avoided without compromising on a reduced output and exports, it is necessary to increase farming productivity. This, however, cannot be done as the need arises. Public policies are necessary and should be planned well ahead. It is necessary to identify the needed and sufficient improvements in productivity that allow for meeting future farming demand with the current levels of land supply available for agriculture and pasture. In case of assessing possible reforestation policies, it is also necessary to address the consequent needed increase in productivities that will lead to the demand being met.

Our project is then constructed in two main phases. First, we need an accurate mid to long-term projection for the baseline output of the main agricultural crops and livestock in Brazil, with occasional deforestation. This will serve as a means to assess the natural development of internal and external farming demand to unfold. Since we wish to assess how a restricted (by policy) land supply will affect total output in the future, it is necessary to build a model relating those variables, whose relationship is by no means linear, as total output depends on a range of different internal and external factors. While other models use static methods such as time series to make output forecasts, they do not allow this scenario simulation, which is the core of our project. We therefore use neural networks to capture those intrinsic relationships between inputs and farming output. This way, we are able to simulate what would happen to production if we tweak the input drivers by policy-making to achieve our sustainability goals.

Lastly, we are left with the task of assessing an optimal set of productivity gains necessary for future scenarios without deforestation and with reforestation. This will hopefully be an essential tool to guide public policy today towards a future both sustainable and prosperous.

Student Project – Agricultural productivity pathways to avoid deforestation in Brazil: application of neural networks

SGI undergraduate student Matheus Mansour has been working on a project relating to agricultural productivity pathways to avoid deforestation in Brazil.

Many models are created with the objective of estimating some kind of economic output, either by a country’s industry or agricultural sector. Time series, general and partial equilibrium models and many other methodologies have been used in the past. However, with the advent of new deep learning methods, powerful tools could be of great use in planning and economic forecasting. In Brazil, a considerable share of GDP is produced by the livestock and agriculture sectors, which have considerable environmental impacts on land use-related issues such as deforestation and biodiversity loss. To avoid these impacts, it is necessary to plan ahead and identify the necessary improvements in productivity for the long ran, if deforestation is to be avoided.

Using data from the last 35 years and 11 of the most important agricultural crops and livestock in Brazil, neural networks will be trained and used as basis for the analysis of scenarios of productivity gains necessary to avoid deforestation in the country and evaluate how reforestation could affect the supply of future agricultural demand. This project is being developed in partnership with Prof. Celma Ribeiro of University of São Paulo, Brazil.

Student Project – Inserting lignin in the sugarcane mills product portfolio: A study using robust optimization approach

SGI PhD student Raphael Dutenkefer has been working on a project looking at insertion of lignin in the sugarcane mills product portfolio.

The use of residues from the sugarcane in industry has been of considerable interest in the last decade. There is a great interest in producing high added value products from residues that today are used solely for the generation of electricity. Lignin, one of the components of lignocellulosic residues derived from sugarcane, is a class of complex organic polymers that can serve as feedstock for the production of many chemicals, materials and even energy carriers. However, its processing technologies are still in an immature technological phase and need further development to become an economically viable option for producers and consumers.

In partnership with Prof. Celma Ribeiro of University of São Paulo, Brazil, this project intends to deeper understand how lignin could improve the economic efficiency of sugarcane mills and what are the best processes being developed today, from an economic perspective. Using a methodology to define the best portfolio for a certain range of products, this project intends to evaluate the investments, maintenance costs, selling price and efficiencies necessary to make lignin a viable feedstock for materials, chemicals and energy carriers.


A model for cleaner power production to defend the blue skies in China

This article was written by Siyuan Chen, a Ph.D. student from Tsinghua University in China who is currently on a placement at the Sustainable Gas Institute. Siyuan in this article describes an energy model that his team is working on that aims to address the huge air pollution problem in China.

The haze weather in Beijing. (Source:

Air pollution problem in China

Over the last few years, air pollution has become a severe problem in China, especially the haze problem. From  2013 to 2016, Beijing experienced haze weather for 183 days each year on average. The wide range of haze weather causes many problems including traffic jams, flight delays, and increasing respiratory disease. There are many reasons for the severe air pollution problem in China, which include vehicle exhausts, construction dust, factory fumes, and coal combustion. However, coal combustion is considered as the major contributor to air pollution, and in China, more than half of the coal consumption is for electricity generation.

Therefore, cleaner production in the power sector plays an important role in tackling the air pollution problem. So how can China ensure it reduces the environmental impact of power generation, and how can energy systems modelling help?

Coal power plant (Source: Pixabay)

Action plan for air pollution prevention and control

In order to solve this urgent air pollution problem, the Chinese government launched the “Action plan for air pollution prevention and control” in 2013. The action plan aims to reduce the inhalable particulate concentration by over 10% in 2017 compared with 2012 levels. At that time, ultra-low emission technologies of coal-fired power plants were developed and first deployed in 2014. Sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions from coal-fired power plants equipped with these emission control devices are lower than 35 and 50 mg/m3 with 6% oxygen content respectively, which is as clean as gas-fired power plants. In order to address the air pollution problem, the Chinese government plan to retrofit all qualified coal power plants with ultra-low emission technologies by 2020.

However, the policies are still vague and the impacts of this change are unknown. So it is essential to find a cost-effective clean production pathway for China’s power sector to address the air pollution problem.

Power generation expansion planning considering environmental issues

Power generation expansion planning is used to determine the optimal type, location, and construction time of power generation technologies whilst ensuring that the increasing power demand is met. Recently, environmental issues have been taken into consideration due to the growing concern of global warming and air pollution.

To deal with China’s air pollution, it is important to conduct power generation expansion planning with environmental constraints. At Tsinghua University, we have developed a model, known as the Long-term Multi-regional, Load-dispatch and Grid-structure based power generation planning model (LoMLoG), to support the decision-making process to help planners understand the environmental issues.

The model takes into account the following four factors:

Wind resources are mainly located in western and northern China (Source: Sino-Danish RED Prog).

1. Uneven distribution

Natural resource and electricity demand in China have an uneven spatial distribution. China has abundant resources in western areas, such as fossil fuel and non-hydropower renewables in Xinjiang and Inner Mongolia and hydropower in Yunnan and Sichuan. However, power demand in eastern coastal areas (e.g. Shanghai, Jiangsu, Zhejiang, and Guangdong) is much greater than in these resource-rich regions. Based on these regional characteristics, China is divided into seventeen areas reflecting power demand and natural resources.

2. Power transmission

With the rapid development of long-distance Ultra-High-Voltage power transmission lines in recent years, eastern coastal areas of China are capable of importing electricity from western areas which have abundant natural resources, instead of constructing power generation facilities locally. Long-distance cross-region power transmission options could have a great influence on regional power generation structure and give new insights to policymakers for air quality control. Therefore, we have included power transmission among regions in this model.

3. A temporal module

Electricity demand has high volatility in a 24-hour period on a day-to-day basis, and also from season-to-season. It, therefore, needs an accurate and reliable electricity supply to match the needs. From the electricity supply side, renewable energy also has a high temporal variation and can be used only when resources are available, which increases the uncertainty of the power system. In order to handle this problem, a temporal module is introduced. We have therefore divided each year into four seasons and each day is divided into twenty-four hours to capture the high time resolution of the power system.

The capacity mix of power sector in all regions is shown.

4. Emissions targets

According to the 13th Five-year Plan for Eco-environmental Protection issued by the State Council, national sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions must be reduced by 15% in 2020 compared to 2015 levels. The emission reduction targets set by the government are therefore also incorporated into the model as must-achieve goals so that air pollution can be controlled.

Cleaning up the power sector

Our model presents a cleaner way for the power sector to reduce and control air pollution. The results show that ultra-low emission coal power plants would account for 60.5% of total coal power plants by 2020. The capacity of renewable energy (wind, solar PV, hydropower) would account for 36% of total power generation units. Due to the large-scale deployment of ultra-low emission technologies in coal power plants and rapid growth of renewable energy, SO2, and NOx emissions would decrease by 44% and 21% in 2020 compared to 2016 levels.

The regional capacity expansion pathway of power sector is also shown in the results. Thanks to the construction of long-distance Ultra-High-Voltage power transmission lines across China, eastern coastal areas with greater air pollution could import a great deal of electricity from western and Northern China, which helps them to decrease local coal power generation and air pollutants emissions accordingly.

Future work

Air pollution control is generally the short-term goal of China’s energy system. In the long term, climate change issues will need more attention. China has made a firm commitment in the Paris Agreement and has become an important participant, contributor, and torchbearer in the global endeavor for environmental civilisation. Therefore, it is vital to find a low-carbon transition pathway for China’s power sector, which would be the focus of future work.

Sustainable Gas Institute (SGI) has developed a global whole system model (MUSE) to simulate energy transitions towards a low carbon world. The model has rich types of technology and novel modelling methods, which can be a good reference for China’s low-carbon energy transition. During my research stay in SGI, I would like to learn these advanced methodologies and conduct cooperative research work on China’s low-carbon energy transition pathway.


BLOG: The importance of decarbonising transport for meeting global emission targets

Dr. Francisca Jalil, a Research Associate at the Sustainable Gas Institute shares some insights from this year’s Sustainable Gas Research and Innovation 2018 conference.

In late September 2018, I attended the Sustainable Gas Research and Innovation (SGRI) conference, at the University of São Paulo, in Brazil. Our Institute hosts this conference every year with the Research Centre for Gas Innovation with the theme being around reducing the environmental impact of natural gas and also addressing topics such as  Carbon Capture & Storage (CCS), and other carbon sequestration, storage, or usage technologies. The conference was especially interesting for me because of the range of topics from very detailed and local technological solutions (or process designs) to global energy systems models, in which the whole world’s energy sector and economic activities are modeled in long-term horizons.

One of the main things that caught my attention at this conference were the number of topics around the decarbonisation of transport. Reducing emissions and the carbon intensity of transport is an urgent matter for meeting the 1.5-2oC targets. It is also the topic of our Institutes next White Paper.

According to a recent report by the Mobile Lives Forum, after the energy sector, the transport sector is the second largest emitter of greenhouse gas (GHG) emissions. However, while world energy use in the power sector is decreasing, transport emissions continue to grow and might become the largest emitter by 2050. But it is also a very complex sector to decarbonise because of the lack of alternative low carbon technologies, especially for aviation, shipping, and heavy-duty road transport. These sectors are also very under-regulated when compared to light duty vehicles, for example being subject to very low or no fuel taxation. In addition to these complexities, transport is also a challenging sector to model compared to other economic sectors, as it has mobile demands. This is especially true for passenger and individual transport, as re-fuelling locations and times are always varying. This presents an additional challenge for modelling transport compared to -for example- heat demand in buildings, where locations are fixed and loads are predictable along days and seasons.

At the conference, I listened to several presentations on decarbonising transport, particularly road freight and shipping. According to the aforementioned report by the Mobile Lives Forum, out of the 14 countries studied Brazil had the second highest share of carbon emissions associated to transport in relation to the total carbon emissions of each country. This is, the transport sector in Brazil accounts for 44.8% of the country’s total carbon emissions, just closely after New Zealand with 44.9%.

The first talk that caught my attention was about how current road freight transport is so heavily reliant on diesel. The investigators used life cycle assessment to contrast this current scenario with natural gas and other diesel alternatives as potential substitutes. The study concluded that natural gas as an alternative fuel produces a quarter of diesel’s carbon emissions and almost no air pollutants at combustion point and that natural gas outperforms diesel in all environmental indicators studied.

Another talk highlighted research that compared liquefied natural gas (LNG) as a shipping fuel, with heavy fuel oil (HFO), marine diesel oil (MDO) and methanol from natural gas. The researchers concluded that as long as methane emissions produced in the engine and supply chain of LNG are controlled and kept under a certain limit, LNG as a shipping fuel can produce lower climate impacts compared to liquid fuels across all timescales. However, they emphasise the need to avoid supply chains with high embodied emissions of methane (this is, methane emitted throughout the processes associated with the whole extraction/production, transport, delivery, and use of fuels).

The third talk was about the use of natural gas in heavy goods shipping. The study emphasised that when looking at natural gas in shipping for diminishing GHG emissions and air pollution, it is very important to analyse the different engine types- which produce varying emissions or benefits across the range- together with the supply chain, to avoid embodied emissions. The researchers also proposed future policy options to regulate or incentivise certain production routes, in order to take advantage of cleaner methods.

Overall, the take-home messages for me were:

1) It is imperative to reduce emissions associated to heavy duty transport to stay on the path of our emission reduction goals.

2) When comparing fuel decarbonisation alternatives, it is very important to analyse not only different engine and fuel types, but also to take into account the different fuel production routes and life cycle emissions as these can have big impact.

3) As highlighted and recommended by one of these researchers and by the IEA, policies on transport “must raise the costs of owning and operating the modes with highest GHG emissions intensity to stimulate investments and purchases of energy-efficient and low-carbon technologies and modes”. This is, policies in transport need to be oriented towards making it expensive to use high-emitting transport modes, and cheaper to switch to cleaner transportation modes.

About Sustainable Gas Research & Innovation conference

The Sustainable Gas Research & Innovation 2018 conference brings stakeholders together to meet; share knowledge, exchange ideas, gain insight, and showcase expertise to fully understand the role of natural gas in the global energy landscape. This year’s event was included in the schedule of the Brazil-United Kingdom Year of Science and Innovation.

About Francisca

Francisca joined the Sustainable Gas Institute in May 2018, after completing her PhD in Chemical Engineering at Imperial College London. Francisca also holds an MSc in Sustainable Energy Futures from Imperial, and a Mechanical Engineering degree and MSc in Mechanical Engineering from Universidad de Chile.


VIDEO: My research in a nutshell – Sandro on reducing industry emissions

How to reduce emissions from industry?

By the time you finish your masters, you’ll know your thesis inside out. We challenged one of our researchers at the Sustainable Gas Institute to explain their research in a short one minute video as part of the ‘Research in a Nutshell Series’.

Sandro Luh is a visiting Masters student from the ETH Zurich. He is using the MUSE energy systems model to examine the potential of different strategies for reducing CO2 emissions in the industrial sector. This includes measures such as fuel switching, electrification and Carbon Capture & Storage.

The industrial sector is a key sector to decarbonise as it accounts for 24% of the total global CO2 emissions (2014).

If you want to find out more about Sandro’s work, read our short interview with him.

VIDEO: Developing the transport model for a Global Energy Systems Model (MUSE)

This short two minute film features Arnaud Koehl, a PhD student at the School of Public Health, at Imperial College who also works at the Grantham Institute and at the Sustainable Gas Institute (SGI).

Arnaud studied International Relations in France and Environmental Economics at UCL. He is now exploring the the kind of sustainable transport policies that could co-benefit health and the economy while addressing climate change.

In this short film (a series of three student films), Arnaud describes his motivations for working in the area of energy and climate change.

He also talks about working on the transport module of the MUSE (Modular Universal energy system Simulation Environment), which is a new model that SGI is developing to analyse energy systems at a global level.