diff --git a/doc/source/Model_input_output.rst b/doc/source/Model_input_output.rst
index 2030fe7..00a16c7 100644
--- a/doc/source/Model_input_output.rst
+++ b/doc/source/Model_input_output.rst
@@ -325,25 +325,25 @@ The output file contains the following variables:
   * - cost_v [dollar]
     - Total cost (including total investment cost, total variable OM cost, and total fixed OM cost) over the planning period.
 
- * - cost_breakdown_v [dollar]
+  * - cost_breakdown_v [dollar]
     - Breakdown of total cost (including total investment cost, total variable OM cost, and total fixed OM cost) over the planning period, by zone, year, and technology.
 
   * - cost_var_v [dollar]
     - Total variable OM cost (including technology variable OM cost, transmission line variable OM cost, and fuel cost) over the planning period.
 
-- cost_var_breakdown_v [dollar]
+  * - cost_var_breakdown_v [dollar]
     - Breakdown of total variable OM cost (including technology variable OM cost, transmission line variable OM cost, and fuel cost) over the planning period, by zone, year, and technology.
 
   * - cost_fix_v [dollar]
     - Total fixed OM cost (including technology fixed OM cost, transmission line fixed OM cost) over the planning period.
 
- * - cost_fix_breakdown_v [dollar]
+  * - cost_fix_breakdown_v [dollar]
     - Breakdown of total fixed OM cost (including technology fixed OM cost, transmission line fixed OM cost) over the planning period, by zone, year, and technology.
 
   * - cost_newtech_v [dollar]
     - Total investment cost of technologies over the planning period.
 
- * - cost_newtech_breakdown_v [dollar]
+  * - cost_newtech_breakdown_v [dollar]
     - Breakdown of total investment cost of technologies over the planning period, by zone, year, and technology.
 
   * - cost_newline_v [dollar]
@@ -361,7 +361,6 @@ The output file contains the following variables:
   * - spillflow_v [m3/s]
     - Spilled water flow of different reservoirs.
 
-
 Execute various scenarios
 -------------------------
 By employing command-line parameters, you can execute different scenarios using the model. For example, if you wish to run a scenario referred to as "low demand," you can prepare input data named ``demand_low.xlsx``. Subsequently, when running the model, you can utilize command-line parameters to specify the scenario value. For instance, you can execute the model by executing the command ``python run.py --demand=low``.