## Inspiration

After looking at the Hack the 6ix prizes, we were all drawn to the BLAHAJ. On a more serious note, we realized that one thing we all have in common is accidentally killing our house plants. This inspired a sense of environmental awareness and we wanted to create a project that would encourage others to take better care of their plants.

## What it does

Poképlants employs a combination of cameras, moisture sensors, and a photoresistor to provide real-time insight into the health of our household plants. Using this information, the web app creates an interactive gaming experience where users can gain insight into their plants while levelling up and battling other players’ plants. Stronger plants have stronger abilities, so our game is meant to encourage environmental awareness while creating an incentive for players to take better care of their plants.

## How we built it + Back-end:

The back end was a LOT of python, we took a new challenge on us and decided to try out using socketIO, for a websocket so that we can have multiplayers, this messed us up for hours and hours, until we finally got it working. Aside from this, we have an arduino to read for the moistness of the soil, the brightness of the surroundings, as well as the picture of it, where we leveraged computer vision to realize what the plant is. Finally, using langchain, we developed an agent to handle all of the arduino info to the front end and managing the states, and for storage, we used mongodb to hold all of the data needed.
[backend explanation here]

### Front-end:

The front-end was developed with **React.js**, which we used to create a web-based game. We were inspired by the design of old pokémon games, which we thought might evoke nostalgia for many players.

## Challenges we ran into

We had a lot of difficulty setting up socketio and connecting the api with it to the front end or the database

## Accomplishments that we're proud of

We are incredibly proud of integrating our web sockets between frontend and backend and using arduino data from the sensors.

## What's next for Poképlants

* Since the game was designed with a multiplayer experience in mind, we want to have more social capabilities by creating a friends list and leaderboard
* Another area to explore would be a connection to the community; for plants that are seriously injured, we could suggest and contact local botanists for help
* Some users might prefer the feeling of a mobile app, so one next step would be to create a mobile solution for our project
## Inspiration & Instructions

We wanted to somehow guilt people into realizing the state of their bank accounts by showing them progressive picture reminders as their wallpaper. Hopefully, the people who use our app will want to save more and also maybe increase their earnings by investing in stocks, SPROUTING personal monetary growth.
To use our app, you can simply install it on your phone. The APK link is below, and it is fully functional. When you first open Sprout, we ask for your bank account information. We then take you to the next screen which will show your current balance and let you set your baseline and goal amounts for your balance. Below that is the current status of your representative plant’s health based on these amounts. Be sure to check the toggle to change the wallpaper of your phone to the plant so that you’re always aware! You can also navigate to a “How To Invest” page from the menu where you can get up-to-date analytical estimations of how you could earn more money through investing.
For a detailed demo, please see our video.

## What it does

Sprout is an Android app to help students and the general populace know how their bank account is doing. It basically sees your current balance, takes the minimum threshold you don’t want your balance to go under and what you’d love to see your balance to be above. Then, the app shows you a cute plant representing the state of your bank account, either living comfortably, living luxuriously, or dying miserably. It will update your phone background accordingly so that you would be able to know at all times. You can also get to a “How To Invest” page, which can briefly educate you on how you could earn more money through investing.

## How we built it

Two of us had experience with Android Development, so we decided we wanted to make an Android app. We used Android Studio as our IDE and Java as our language of choice. (For our plant designs, we used Adobe Illustrator.) To simulate information about a possible user’s account balance, we used the MINT API to fetch financial data. In order to incentivize our users to maybe invest their savings, we used the NASDAQ API to get stock information and used that to project earnings from the user’s balance had they invested some of it in the past. We offer some brief advice on how to start investing for beginners as well.

## Challenges we ran into

Random small bugs, but we squashed the majority of them. Our biggest problem was thinking of a good idea we would be able to implement well in the time that we had!

## Accomplishments that we're proud of

Our app has many features and a great design!

## What we learned

We can get a lot done in a short amount of time :^D

## What's next for Sprout?

Background app refresh to automatically check as transactions come in so that the most accurate plant can be shown.

## Built With

* Java
* Android Studio
* NASDAQ API
* Mint API
* Adobe Illustrator (for Designs)

## Try it out

Link to APK: <https://github.com/zoedt/yhack-2016/blob/master/app-debug.apk>
## Inspiration

As the imperative to reduce carbon emissions intensifies, businesses are increasingly adopting innovative approaches to shrink their carbon footprint. One such approach, carbon offsetting, has grown into a [$5.5 billion industry](https://journals.library.columbia.edu/index.php/cjel/article/view/10442), garnering support from both fossil fuel companies and environmental advocates alike. Rather than curbing emissions directly at their source, carbon offsets mitigate carbon pollution by either preventing potential emissions or directly extracting carbon dioxide from the atmosphere, often employing technologies such as carbon capture and storage (CCS) or direct air capture (DAC). Presently, numerous critical sectors—such as the aviation industry—face challenges in achieving decarbonization due to financial constraints, material limitations, and a lack of innovative solutions. **As such, the combination of decarbonization strategies and carbon offsetting is often the most economical; by reducing easily avoidable emissions and offsetting the rest, companies can achieve net-zero emissions without the financial pressures of complete emissions reductions.**

However, there are three existing barriers to achieving this diversified net-zero strategy. First, reducing emissions is often a lengthy process, often taking years to decarbonize key components of a company’s business model fully. Second, reducing low-cost emissions is dependent on the equilibrium price of carbon offset—a price that often needs to be calculated far in advance to successfully execute decarbonization. Third, the financial tradeoff between the price of offsets and emissions reductions is blurry, with no clear brightline between offset and reduced emissions.

## What does our project do?

Our product solves these issues by providing key insights about decarbonization strategy through forward pricing curves for carbon offsets. By calculating the expected demand and supply of carbon offsets for a given time in the future, the market equilibrium—the going price of carbon offsets at that time—can be estimated and given to the user in the present. Our results are displayed on a website dashboard.

Our project contains two central components: the market price of carbon credits over time, based on calculated demand and supply curves, and a carbon-zero pathway based on these curves (based on user inputs and other external data). The demand and supply curves are generated for each year until the user’s target year, this allows them to see how the market changes year-by-year, allowing them to optimize their offset buying distribution over a certain period. The carbon-zero pathway allows

After inputting information about a company’s carbon footprint, emissions targets, and the marginal cost of abatement, financial metrics about the best carbon-zero path are computed. The output of this function relies on market equilibrium outputs only available after computing the forward price curves. The output gives the user valuable information about the percentage breakdown between decarbonized emission reductions and credit offsets, and the financial projections of both strategies.

This tool, importantly, allows companies to see how the market price of carbon credits changes over time based on user-given inputs such as the type of carbon removal technology, the year of their net-zero goal, and the scope of emissions they are considering. This tool emphasizes the importance of decarbonization by reorienting the goal of maximizing profits towards environmental advocacy. This is key in assisting companies plan their emissions-reduction strategy in the most economical way—two priorities that have often been at odds with each other.

## How we built our project

We built all of our projection models in Python and used the Reflex framework to display our outputs in a user dashboard. Each of the three system functions—the demand curve, supply curve, and decarbonization-offset tradeoff scheme—are integrated into the user dashboard, with various inputs related to the computation of the base futures curve model and tradeoff model being imputed as fields. Each of the three system functions are described below:

The demand curve uses the emissions profiles of the top 2,000 largest companies in the world to project their future carbon emissions based on their emissions reduction goals. Using generated GICS sector data for industry-specific emissions—combined with company revenue—we project yearly emissions—including scope 1, 2, and 3 emissions—depending on carbon reduction goals. Using an exponential Pareto distribution—tuned to represent companies buying offsets closer to their consumption date—we generalize the year-to-year demand for carbon permits on a per-company basis. This data returns the expected demand for carbon credits per year for a theoretical global market. Using this data, combined with decarbonization prices generalized from half-normal, sector-level decarbonization costs, our model produces the expected carbon demand for a given period for each company. As each company faces a different cost of decarbonizing their business, the marginal benefit of each company is unique. We fit a demand curve to these projected quantity and price demands from individual companies using polynomial regressions from this data. This process is done for each year in the model.

The supply curve is interpolated from the technology pathway of the carbon credit and price range. Based on our research, most companies pay anywhere from $50/ton CO2 to $700/ton CO2 for a range of carbon credits. This data is combined with the cost of implementing the selected pathway for a single ton of CO2 and the ranking of the industries based on their relative CO2 emissions. A coefficient is calculated on these values that inform the supply curve on the scale of 50$/tCO2 to 700$/tCO2. We create a supply curve for each year until the user-specified target year with the values being adjusted for the following years accordingly.

The decarbonization-offset tradeoff graph is produced by mapping another exponential Pareto distribution to the marginal cost of reducing carbon emissions. The resulting intersection of the market price of carbon offsets for a given year with the CDF returns the percentage of total emissions for which it is more economical to reduce the production of emissions over buying offsets, and vice versa. The total cost associated with purchasing carbon offsets, alongside the quantity, can be calculated for a given company’s carbon footprint.

## Challenges we ran into

The biggest challenge we ran into was the lack of data for building the supply curves. One way in which we handled this was by taking data corresponding to the maximum and minimum prices, as well as the range of carbon credits companies were buying in bulk. We used these to generate a pseudo-supply curve that would allow us to reasonably estimate the curve as if we had the data. We expect this problem to be solved as more data becomes available.

## What we learned and accomplished

We learned a lot about time-series analysis, data analysis, and synthesizing, as well as how to use Reflex to implement our website. We made a working model and website on a project that aimed to encapsulate the entire carbon credits market for the next few decades.

## What's next for CarbonInsights

Short Term: The biggest thing we would like to change is to make the website dynamically update. Although the models we wrote *are perfectly suited to update dynamically*, the Reflex framework we are using has made that difficult.

Long Term: Improve the statistical models to create the projection for the supply-demand curves and use ARIMA, ETS, and ML models like Random Forests to validate and provide a more detailed analysis of the trends.