diff --git a/docs/source/approach.rst b/docs/source/approach.rst
new file mode 100644
index 0000000..a451a5a
--- /dev/null
+++ b/docs/source/approach.rst
@@ -0,0 +1,88 @@
+.. _approach:
+
+Approach and Implementation
+===========================
+
+There are several approaches that have been proposed and/or implemented to reduce the carbon footprint 
+of computing. These include interventions in procurement to maximise the useful life of computer 
+hardware and thus reduce the embodied carbon cost; exercises in code modification to reduce the 
+carbon cost of a particular calculation, mostly by shortening the runtime or reducing resource needs; 
+modifying default settings to reduce carbon use [e.g. the clock frequency on ARCHER2, the UK national 
+supercomputer, is now lower by default, @Jackson23]; and attempts to improve data centre efficiency. 
+However, many of these can be difficult for individual researchers to implement. One 
+easier-to-implement approach is to make use of lower carbon intensive electricity to power the 
+computation. This can be achieved either by relocating the computer to a part of the world where 
+electricity is generated using renewable means [e.g. the University of York in the UK have located 
+the latest institutional computing resource in Sweden @Norsecode], or by time shifting the computation 
+such that it runs when the power supplied by the local electricity grid is dominated by renewable 
+generation such as on windy or sunny days rather than periods where legacy fossil fuel generation 
+dominates. The potential impact of minimally-invasive time-shifting approaches has been shown to 
+result in significantly reduced carbon footprints [27% in one AI benchmark @Dodge2022]. In a country 
+like the UK, these reductions can reach over 60% by shifting workload by a day or two [@ElectricityMaps]. 
+CATS helps researchers timeshift their own computation such that it is scheduled when the forecast 
+carbon intensity of the power grid is minimised.
+
+At its core CATS is an open-source (MIT licence) Python package (tested with Python 3.9-3.12) that 
+combines data on the forecast carbon intensity of the local electricity supply with information about 
+a proposed job's duration to assess the best start time of the computation within the validity interval 
+of the forecast. Users typically interact with CATS via a command-line interface targeting the UNIX 
+Shell (CATS is tested on Linux and MacOS) and the best start time can be provided in an informative 
+format (that the user can then use with their infrastructure) or in a way that can be passed on to job 
+schedulers to set the calculation start time. CATS is available via the Python Package Index (PyPI) 
+and can be installed along with its handful of dependencies into a Python environment with pip. 
+Development takes place on GitHub (https://github.com/GreenScheduler/cats) and documentation is
+ available (https://greenscheduler.github.io/cats/).
+
+At a minimum, the user must provide CATS with the duration of the proposed computation on the command 
+line and CATS also requires information about the location where the computation is to happen. This 
+can be provided on the command line, via a configuration file, or from geolocation of the IP address 
+and is sufficient information for CATS to access a prediction of the carbon intensity of the relevant 
+power distribution network which is used to compute the start time that minimises the carbon intensity 
+over the duration of the computation.
+
+At the moment, CATS is available for the UK through the National Grid's API which provides 
+postcode-specific 48 hour forecasts broken down into 30 minute periods. This granular data contains 
+regional distribution networks making use of a parameterised model of the power distribution system 
+in Great Britain, weather forecasts and historical generation data. A brief overview of the forecast 
+methodology can be found in [@Bruce21a; @Bruce21b] and the forecast, in the form of estimated carbon 
+intensity at the end of each half hour period, is available via a web API 
+(https://carbonintensity.org.uk/). CATS caches requests for this data to avoid repeated requests 
+within the thirty minute time frame of a single forecast. We have designed CATS in a modular way to 
+enable future integration of other countries' APIs in a straightforward manner. However, to date, we 
+are unaware of any publicly accessible forecasts of carbon intensity for other regional, national or 
+transnational electricity distribution networks.
+
+With the carbon intensity forecast and duration of the proposed computation in hand, the next task 
+is to locate the start time (within the valid forecast period) that minimises the integrated carbon 
+intensity over a sliding window equal to the duration. All else being equal, this minimising start 
+time is the time when the carbon cost of the proposed computation will be least. The difference in 
+the integrated carbon footprint between an optimal start time and starting the calculation immediately 
+is a measure of the potential benefits of timeshifting the computation. An illustration of this 
+calculation is provided in Figure 1. Once the carbon intensity minimisation has been completed, 
+CATS can optionally submit the computation to a queueing system or make a more detailed report on the 
+climate impact of the proposed computation.
+
+Submitting the computation to a queueing system can take just one more step, most clearly illustrated 
+by using the veritable `at` program available on most Linux and MacOS systems. The `at` program 
+“queues jobs for later execution” and, assuming the user provides the command to run their computation 
+and any necessary arguments using the CATS `--command` option, the computation can be scheduled to run 
+at the optimal time through delayed start activated with the `at` CLI. The options for the time syntax 
+for `at` on different systems are mutually incompatible, so CATS restricts itself to the POSIX 
+compatible time format. CATS also supports job submission to other queuing systems such as slurm.
+
+![illustration of the carbon intensity time series for (blue) with the predicted energy use for 
+running a twelve hour calculation now (red) or at a time that minimises the integrated carbon 
+intensity (green).\label{fig:schedule}](fig1.png)
+
+Providing further information about the carbon cost of the proposed computation improves the 
+educational impact of CATS. This can be enabled with the `--footprint` command-line option. 
+This calculation follows that used by the Green Algorithms project [@Lannelongue21], which also 
+provides some easy to understand “equivalent” statements to put these numbers in context. To 
+provide this footprint information CATS must be configured with information about the hardware 
+(principally the per-core power consumption of the processors) via “profiles” in the configuration 
+file. Different profiles can allow for different classes of computation (for example, one that 
+only uses the CPU, or one that uses the CPU and an attached GPU). This information, together with 
+the grid carbon intensity and job duration, allows the total power consumption of the computation 
+to be estimated along with the implied CO$_2$ cost if it were to be run now, or if the start time 
+were to be delayed to minimise the carbon intensity. In addition, this information can be included 
+in graphical output.
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 165bbe9..dc7792b 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -14,6 +14,7 @@ cluster jobs to minimize predicted carbon intensity of running the process.
    introduction.rst
    installation.rst
    quickstart.rst
+   approach.rst
    use-with-schedulers.rst
    cli-reference.rst
    api-reference.rst