Last year I invested in some remote vacant land and posted 3 blogs on the topic of remote land investing (Part 1, Part 2, Part 3).

Today I want to discuss one other benefit of investing in remote land, namely, that payments to this network were used to purchase 130 acres of remote land that is helping to offset my own CO2 usage and the CO2 usage of those who paid any fees.

I certainly use fossil fuels in my farming activities, but I'm not running larger tractors that often. String trimmers, lawn tractors, lawn mowers and wood chippers are the main consumers of fossil fuels.

I use a small truck that burns fossil fuels to gather supplies, run errands and travel back to my remote land.

I haven't taken a flight in years because me and my wife are too busy and there is no place I would rather be in spring, summer, fall.

Even though I am not an angel when it comes to burning fossil fuels, I believe that the 130 acres of land purchased with funds raised from this network, are offsetting my own usage and the usage of entrepreneurs who pay fees to this network. The land is sequestering carbon in new forest growth, in fields that are not plowed or mowed, and through a type of farming (managing wild blueberries) that does not require heavy use of fossil fuels.

To get to my remote property, you need to travel along a road called Melrose Hill Road. This takes you to a turn off onto my property alongside Beverly Hills Road. Here is what it looks like as you travel through Beverly Hills.

The ground is white with lichen, an indicator of high air quality. A close up of the trees reveals the growing tips that are taking carbon out of the atmosphere to create new growth.

If one were to take samples of the soil in some of the abandoned fields, I would expect to see the amount of soil organic matter increasing over time as the grass and weeds grow and die back each year contributing to the amount of carbon sequestered into the soil.

When I purchased this land, it was not done with the intention of storing carbon but that is a very real benefit that often comes with remote land investing. The funds used to purchase that land came from entrepreneurs who paid my fees last year and from entrepreneurs in the future who are helping me to pay down the remaining loan that was used to purchase the land. Your funds are making a difference in the amount of carbon in the atmosphere.

Is this a green network? I think so and while I don't use all the funds from the network to pay for carbon offsetting land, a significant amount was and that land is converting atmospheric carbon into new tree growth and soil organic matter. I can't tell you how much carbon is being sequestered as I haven't tried to do the math. A book that might have some answers is The Carbon Farming Solution (2016) by Eric Toensmeier.

This weekend I started construction on a modest 8ft x 8ft outpost in a wild blueberry field (Vaccinium Angustifolium) where I hope to better observe the land and perhaps do some calculations on how much carbon is being offset.

I recently borrowed an academic book from a local university called Agent-Based Modeling of Environmental Conflict and Cooperation (2019) by Todd K. Bendor and Jurgen Sheffran. The cover image depicts a place 20 km from Cape Town South Africa with poor housing and services on the left, rich gated housing on the right and no man's swamp land in the middle.

The main reason I picked up the book was because it looked like it had some useful applications of systems thinking to an important topic: modelling conflicts to help resolve conflicts. I like the fact that it is not an edited volume and provides a consistent perspective and level of quality on a variety of interesting topics in conflict modelling and conflict resolution. There is some math in the book, which I also like, because it is used to clarify and add precision to important ideas, not to prove theorems and other niceties. There are some practical agent-based modelling applications discussed in Part III of the book which is where a good chunk of value of this book lies in my opinion.

Table of Contents

Part I: Conflict and the Promise of Conflict Modeling

1. Environmental Conflicts in a Complex World

2. Why Model? How Can Modeling Help Resolve Conflict?

3. The History and Types of Conflict Modeling

4. Participatory Modeling and Conflict Resolution

Part II: Modeling Environmental Conflict

5. System Dynamics and Conflict Modeling

6. Agent-Based Modeling and Environmental Conflict

7. Modeling Conflict and Cooperation as Agent Action and Interaction

Part III: Applications of the VIABLE Model Framework

8. A Viability Approach to Understanding Fishery Conflict and Cooperation

9. An Adaptive Dynamic Model of Emissions Trading

10. Modeling Bioenergy and Land Use Conflict

11. The Future of Modeling Environmental Conflict and Cooperation

You don't have to read the book cover to cover to get something out of it. To maximize your time, you can scan through and find an application or section that interests you and read about that. For example, I came across a section called The Farmer Agent Model on pp. 292 to 295 and decided that it might be worth investing some time into that concept. One component of the The Farmer Agent Model is the harvest function which I have simplified to:

h = r * A * B * f

The harvest for a particular crop h by a particular farmer is found by multiplying:

• r: The fraction of land planted in that crop, also called the priority of the crop.
• A: The arable land area in hectares used for the crop.
• B: The biomass yield per hectacre.
• f: The fraction of biomass produced that is harvested (e.g., 90%)

If you add up the harvest amount for each farmer h harvesting that crop sum(h), then you get the total harvest for that crop H. You can use this sum as the crop supply in agricultural equations of supply and demand to figure out the price you might get for that crop. If prices are low for a particular crop then The Farmer Agent Model might respond by adjusting the value of the crop priority r downward so less land area is devoted to that crop. Using harvest functions, pricing functions, and investments functions in an integrated model allows you to tweak parameters to see how they influence other parts of the overall model. Government investment in ethanol production using subsidies, for example, will increase the value of r for bioenergy crops.

Another interesting topic in Chapter 10: Modelling Bioenergy and Land Use Conflict involves using spatial models of farmers across the landscape so that you take into account spatial interactions among farmers. We don't just treat farmers as disembodied harvest functions, but make an effort to locate those harvest functions (aka farmers) on a grid so you can simulate and explore local interactions that might be important to account for.

Conflicts can arise in communities establishing a bioenergy plant because citizens may have differing views on fuel, water, land use changes that the plants bring with them or signify. Some may object to the increased water use or the nitrogen runoff that might accompany increased corn production next to streams. The other side may object that farmers need to be profitable and the plant jobs are needed. Many of these issues and interactions may be left unmanaged if they are not explicitly modelled as part of an overall model of the bioenergy plant and how it interacts with the ecosystem, the main stakeholders, and the government incentives that might be driving increased production of bioenergy crops.

In Illinois they have some of the best soil and growing conditions for bioenergy crops and other types of crops so bioenergy conflicts are likely more common there. Where I live, there is a conflict around burning wood for energy in a local paper mill. The pro side argues that it helps the mill keep their costs of operations lower because power is a huge cost for them, that we don't have to make expensive upgrades to our power grid to supply the Mill with power, that burning wood for energy is better than burning coal because the wood is renewable, and that it is helping sustain the economy with forestry employment in rural areas and good paying mill jobs. The opposing side argues that the large number of truckloads of wood a day needed to feed the bioenergy plant is not green, that it is decimating forests and habitat, that we can find better uses, that we can be adding more value to our products and keep the same level of jobs and economic prosperity, etc..

Fisheries is a big industry here and there are lots of conflicts between fishermen and the government over quotas, between fishermen and environmental groups over the cause of whale deaths and what needs to be done, between fishermen and another local pulp and paper mill over the mills desire to pipe effluent into fishing grounds. The powerful pulp and paper industry has met its match when their practices affect the equally powerful fishing industry.

Because I have an interest in fisheries (my father-in-law and his sons are fishermen) I was interested in picking up this book to see how they approach modelling conflict in the fisheries (see Chapter 8. A Viability Approach to Understanding Fishery Conflict and Cooperation). The starting point for all the models we might want to create is generally is a simple stock and flow diagram for a single fish population. From here we start drawing other boxes and linkages to capture more of the complexity of the situation.

Agent based modelling often starts with creating visual depictions of the system. Stock and flow diagrams are a commonly used technique but you generally need to master a few more visualization techniques to depict a full systems model. The best intro to these techniques is Thinking in Systems (2008) by Donnella H. Meadows.

The German philosopher Gottfried Leibniz (died 1716) was famous for his slogan "Let Us Calculate". The slogan conveyed his belief that conflicts could be resolved by representing problems in a logical calculus that would help reasonable people find solutions. The belief that we can resolve conflicts through modelling therefore is not a new idea. What is new is that the agent based models (aka "logical calculus") are getting better at incorporating more of the complexity of the situation and providing a better decision aid for resolving conflicts. Leibniz's envisioned logical calculus can be implemented in the multi-agent programmable modeling environment called NetLogo which was used as the agent-based modelling platform for the conflict models discussed in this book. I've toyed with the idea of learning NetLogo, but this book gives me more reasons to do so as the book would be of even greater value if I downloaded and ran their conflict models.

It should be noted, however, that resolving conflicts is often not as simple as coming up with a good conflict model and the value of modelling can be overrated when it is done poorly. It tends towards armchair theorizing if not communicated to and validated by stakeholders in the conflict. That being said, doing armchair theorizing based on NetLogo is better than doing armchair theorizing based on NetFlix.

This is a very limited review of the book to give you a flavor of the content you might find. Not exactly a coffee table book or one that would appeal to a wide audience, or one that is accessible price wise; nevertheless I think it is worth seeking out for those with an interest in exploring the use of agent based models to represent and resolve conflict situations. Also, anyone wanting to add a new book to their systems thinking collection.